index stringlengths 21 21 | question stringlengths 87 406 | A stringlengths 3 170 | B stringlengths 2 280 | C stringclasses 1
value | D stringclasses 1
value | bounding_box listlengths 1 3 | direction listlengths 0 3 | answer stringclasses 4
values | answer_cot stringlengths 385 3.54k | answer_name stringlengths 6 164 | category stringclasses 12
values | image_url stringlengths 20 20 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
00003e2837c7b728_9797 | Consider the real-world 3D locations and orientations of the objects. If I stand at a green stool's position facing where it is facing, is a book with a green spine on the left or right of me? | on the left | on the right | null | null | [
{
"bbox_3d": [
-0.3,
0.5,
1.1
],
"label": "a book with a green spine"
},
{
"bbox_3d": [
0.9,
0.1,
1.6
],
"label": "a green stool"
}
] | [
{
"front_dir": [
0.1,
0,
-1
],
"label": "a green stool",
"left_dir": [
-1,
0,
-0.1
]
}
] | A | To solve this problem, we first determine the 3D locations of a book with a green spine and a green stool. Then we estimate the vector pointing from a green stool to a book with a green spine, as well as the left direction of a green stool. Next we compute the cosine similarities between the vector and the left directi... | A. on the left. | orientation_on_the_left | 00003e2837c7b728.jpg |
0000615b5a80f660_2246 | Consider the real-world 3D orientations of the objects. What is the relationship between the orientations of a metal exhaust hood and a white coffee maker, parallel of perpendicular to each other? | parallel | perpendicular | null | null | [
{
"bbox_3d": [
-1.2,
1.7,
3.4
],
"label": "a metal exhaust hood"
},
{
"bbox_3d": [
-1.5,
1.2,
3.2
],
"label": "a white coffee maker"
}
] | [
{
"front_dir": [
0.4,
-0.1,
-0.9
],
"label": "a metal exhaust hood",
"left_dir": [
-0.9,
0.1,
-0.4
]
},
{
"front_dir": [
0.6,
0,
-0.8
],
"label": "a white coffee maker",
"left_dir": [
-0.8,
0.1,
-0.6
... | A | To solve this problem, we first detect the front directions of a metal exhaust hood and a white coffee maker. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is closer to 0 or 180, than to 90 degrees, then the two object... | A. parallel. | multi_object_parallel | 0000615b5a80f660.jpg |
0000f2101250b009_b7d4 | Consider the real-world 3D locations of the objects. Are the a sheep with a white face and the a herd of sheep in a cage next to each other or far away from each other? | next to each other | far away from each other | null | null | [
{
"bbox_3d": [
-0.7,
0.6,
1.8
],
"label": "a sheep with a white face"
},
{
"bbox_3d": [
0,
0.6,
1.5
],
"label": "a herd of sheep in a cage"
}
] | [] | A | To solve this problem, we first estimate the 3D locations of a sheep with a white face and a herd of sheep in a cage. Then we can compute the L2 distance between the two objects. Next we estimate the rough sizes of the two objects. If the distance between the two objects is smaller or roughly the same as the object siz... | A. next to each other. | location_next_to | 0000f2101250b009.jpg |
0000f8aef032941e_6e04 | Consider the real-world 3D locations of the objects. Are the a man in a white shirt and blue jeans and the a classroom with a group of people next to each other or far away from each other? | next to each other | far away from each other | null | null | [
{
"bbox_3d": [
-2.3,
2.5,
6.3
],
"label": "a man in a white shirt and blue jeans"
},
{
"bbox_3d": [
0,
3.4,
8.2
],
"label": "a classroom with a group of people"
}
] | [] | A | To solve this problem, we first estimate the 3D locations of a man in a white shirt and blue jeans and a classroom with a group of people. Then we can compute the L2 distance between the two objects. Next we estimate the rough sizes of the two objects. If the distance between the two objects is smaller or roughly the s... | A. next to each other. | location_next_to | 0000f8aef032941e.jpg |
0000f8aef032941e_cf26 | Consider the real-world 3D orientations of the objects. Are a black chair and a black chair with a black seat facing same or similar directions, or very different directions? | same or similar directions | very different directions | null | null | [
{
"bbox_3d": [
-1.2,
1.8,
5.4
],
"label": "a black chair"
},
{
"bbox_3d": [
2.3,
1.6,
4.9
],
"label": "a black chair with a black seat"
}
] | [
{
"front_dir": [
0.3,
-0.2,
-0.9
],
"label": "a black chair",
"left_dir": [
-1,
0,
-0.3
]
},
{
"front_dir": [
-0.9,
-0.2,
-0.3
],
"label": "a black chair with a black seat",
"left_dir": [
-0.3,
0.1,
1
... | B | To solve this problem, we first detect the front directions of a black chair and a black chair with a black seat. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is small, then the two objects are facing same or similar ... | B. very different directions. | multi_object_same_direction | 0000f8aef032941e.jpg |
00010417d07870a7_387c | Consider the real-world 3D locations of the objects. Are the a man in a green shirt and khaki shorts walking and the a person wearing a green shirt next to each other or far away from each other? | next to each other | far away from each other | null | null | [
{
"bbox_3d": [
0.3,
1.4,
3
],
"label": "a man in a green shirt and khaki shorts walking"
},
{
"bbox_3d": [
-0.4,
3.1,
19.8
],
"label": "a person wearing a green shirt"
}
] | [] | A | To solve this problem, we first estimate the 3D locations of a man in a green shirt and khaki shorts walking and a person wearing a green shirt. Then we can compute the L2 distance between the two objects. Next we estimate the rough sizes of the two objects. If the distance between the two objects is smaller or roughly... | A. next to each other. | location_next_to | 00010417d07870a7.jpg |
00010bf498b64bab_371f | Consider the real-world 3D locations and orientations of the objects. Which side of a black car is facing the camera? | front | left | back | right | [
{
"bbox_3d": [
-1.7,
0.5,
15.3
],
"label": "a black car"
}
] | [
{
"front_dir": [
0.3,
0,
-1
],
"label": "a black car",
"left_dir": [
-1,
0.1,
-0.3
]
}
] | A | To solve this problem, we first estimate the 3D location of a black car. Then we obtain the vector pointing from the object to the camera. Now we compute the angles between the vector and the left, right, front, back directions. We first compute the left direction of a black car, which leads to the angle between left d... | A. front. | orientation_viewpoint | 00010bf498b64bab.jpg |
00046553cda0c8d7_6186 | Consider the real-world 3D locations and orientations of the objects. Which object is a wooden easel with paintings facing towards, a black hat or the a woman wearing a white coat? | a black hat | a woman wearing a white coat | null | null | [
{
"bbox_3d": [
1.2,
0.9,
6.9
],
"label": "a wooden easel with paintings"
},
{
"bbox_3d": [
0,
1.1,
7.5
],
"label": "a black hat"
},
{
"bbox_3d": [
-0.3,
0.5,
4.4
],
"label": "a woman wearing a white coat"
}
] | [
{
"front_dir": [
-0.4,
-0.1,
-0.9
],
"label": "a wooden easel with paintings",
"left_dir": [
-0.9,
0.1,
0.4
]
}
] | B | To solve this problem, we first detect the 3D location of a wooden easel with paintings, a black hat, and a woman wearing a white coat. Then we compute the cosine similarities between the front direction of a wooden easel with paintings and the vectors from a wooden easel with paintings to the other two objects. We can... | B. a woman wearing a white coat. | multi_object_facing | 00046553cda0c8d7.jpg |
000472189adb5cbd_b785 | Consider the real-world 3D orientations of the objects. Are a black car parked and a red car parked in front of a house facing same or similar directions, or very different directions? | same or similar directions | very different directions | null | null | [
{
"bbox_3d": [
9.8,
1.9,
21.4
],
"label": "a black car parked"
},
{
"bbox_3d": [
6.5,
2.2,
24.5
],
"label": "a red car parked in front of a house"
}
] | [
{
"front_dir": [
0.2,
0.1,
1
],
"label": "a black car parked",
"left_dir": [
1,
-0.1,
-0.2
]
},
{
"front_dir": [
0.3,
-0.2,
-0.9
],
"label": "a red car parked in front of a house",
"left_dir": [
-1,
0,
-0... | B | To solve this problem, we first detect the front directions of a black car parked and a red car parked in front of a house. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is small, then the two objects are facing same o... | B. very different directions. | multi_object_same_direction | 000472189adb5cbd.jpg |
00049f0e30c1dd2a_7710 | Consider the real-world 3D orientations of the objects. What is the relationship between the orientations of a green car and a green car, parallel of perpendicular to each other? | parallel | perpendicular | null | null | [
{
"bbox_3d": [
6.4,
4.5,
98.1
],
"label": "a green car"
},
{
"bbox_3d": [
14,
2.4,
38.8
],
"label": "a green car"
}
] | [
{
"front_dir": [
0,
-0.1,
-1
],
"label": "a green car",
"left_dir": [
-1,
0.1,
0
]
},
{
"front_dir": [
-0.3,
-0.1,
-1
],
"label": "a green car",
"left_dir": [
-1,
0.1,
0.3
]
}
] | A | To solve this problem, we first detect the front directions of a green car and a green car. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is closer to 0 or 180, than to 90 degrees, then the two objects are parallel to ... | A. parallel. | multi_object_parallel | 00049f0e30c1dd2a.jpg |
00050647c857b018_4d34 | Consider the real-world 3D orientations of the objects. What is the relationship between the orientations of a woman sitting on a wooden bench and a wooden chair with a woman sitting on it, parallel of perpendicular to each other? | parallel | perpendicular | null | null | [
{
"bbox_3d": [
0.3,
0.5,
2.4
],
"label": "a woman sitting on a wooden bench"
},
{
"bbox_3d": [
0,
0.3,
1.4
],
"label": "a wooden chair with a woman sitting on it"
}
] | [
{
"front_dir": [
0,
0,
-1
],
"label": "a woman sitting on a wooden bench",
"left_dir": [
-1,
-0.1,
0
]
},
{
"front_dir": [
0.1,
0.2,
-1
],
"label": "a wooden chair with a woman sitting on it",
"left_dir": [
-1,
... | A | To solve this problem, we first detect the front directions of a woman sitting on a wooden bench and a wooden chair with a woman sitting on it. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is closer to 0 or 180, than ... | A. parallel. | multi_object_parallel | 00050647c857b018.jpg |
0005133a0bf2ecc1_8104 | Consider the real-world 3D locations and orientations of the objects. If I stand at a bulletin board with a picture of a vineyard's position facing where it is facing, is a man and a woman standing together in front of me or behind me? | in front of | behind | null | null | [
{
"bbox_3d": [
-0.2,
0.7,
1.8
],
"label": "a man and a woman standing together"
},
{
"bbox_3d": [
0.5,
0.5,
1.6
],
"label": "a bulletin board with a picture of a vineyard"
}
] | [
{
"front_dir": [
0.7,
0.1,
-0.7
],
"label": "a bulletin board with a picture of a vineyard",
"left_dir": [
-0.8,
0.1,
-0.6
]
}
] | B | To solve this problem, we first determine the 3D locations of a man and a woman standing together and a bulletin board with a picture of a vineyard. Then we estimate the vector pointing from a bulletin board with a picture of a vineyard to a man and a woman standing together, as well as the front direction of a bulleti... | B. behind. | orientation_in_front_of | 0005133a0bf2ecc1.jpg |
00056223ec2b5aa1_1be8 | Consider the real-world 3D locations of the objects. Which is closer to a woman in a red shirt sitting in a chair, a seat with a brown leather cover or a train with a large window? | a seat with a brown leather cover | a train with a large window | null | null | [
{
"bbox_3d": [
-0.3,
0.8,
1.9
],
"label": "a woman in a red shirt sitting in a chair"
},
{
"bbox_3d": [
0.6,
0.7,
1.8
],
"label": "a seat with a brown leather cover"
},
{
"bbox_3d": [
0.4,
1.5,
3.5
],
"label": "a train... | [] | A | To solve this problem, we first detect the 3D location of a woman in a red shirt sitting in a chair, a seat with a brown leather cover, and a train with a large window. Then we compute the L2 distances between a woman in a red shirt sitting in a chair and a seat with a brown leather cover, and between a woman in a red ... | A. a seat with a brown leather cover. | multi_object_closer_to | 00056223ec2b5aa1.jpg |
00056dc4f587f43e_9377 | Consider the real-world 3D locations and orientations of the objects. Which side of a black rectangular object is facing the camera? | front | left | back | right | [
{
"bbox_3d": [
0.4,
1.2,
6.7
],
"label": "a black rectangular object"
}
] | [
{
"front_dir": [
0,
-0.1,
-1
],
"label": "a black rectangular object",
"left_dir": [
-1,
0,
0
]
}
] | A | To solve this problem, we first estimate the 3D location of a black rectangular object. Then we obtain the vector pointing from the object to the camera. Now we compute the angles between the vector and the left, right, front, back directions. We first compute the left direction of a black rectangular object, which lea... | A. front. | orientation_viewpoint | 00056dc4f587f43e.jpg |
0005dadf2a8e8bef_886a | Consider the real-world 3D orientations of the objects. What is the relationship between the orientations of a boat with a yellow stripe and a boat with a red hat, parallel of perpendicular to each other? | parallel | perpendicular | null | null | [
{
"bbox_3d": [
-0.4,
0.6,
6.4
],
"label": "a boat with a yellow stripe"
},
{
"bbox_3d": [
0.2,
0.5,
6.5
],
"label": "a boat with a red hat"
}
] | [
{
"front_dir": [
0.1,
-0.1,
-1
],
"label": "a boat with a yellow stripe",
"left_dir": [
-1,
0.1,
-0.1
]
},
{
"front_dir": [
0,
-0.1,
-1
],
"label": "a boat with a red hat",
"left_dir": [
-1,
0.1,
0
]
... | A | To solve this problem, we first detect the front directions of a boat with a yellow stripe and a boat with a red hat. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is closer to 0 or 180, than to 90 degrees, then the tw... | A. parallel. | multi_object_parallel | 0005dadf2a8e8bef.jpg |
0005fd0beaedf55c_fde0 | Consider the real-world 3D locations of the objects. Are the a beak with a brown texture and the a bird with a pink head next to each other or far away from each other? | next to each other | far away from each other | null | null | [
{
"bbox_3d": [
0.4,
0.4,
1.7
],
"label": "a beak with a brown texture"
},
{
"bbox_3d": [
0.2,
0.3,
1.5
],
"label": "a bird with a pink head"
}
] | [] | A | To solve this problem, we first estimate the 3D locations of a beak with a brown texture and a bird with a pink head. Then we can compute the L2 distance between the two objects. Next we estimate the rough sizes of the two objects. If the distance between the two objects is smaller or roughly the same as the object siz... | A. next to each other. | location_next_to | 0005fd0beaedf55c.jpg |
0006567209c36aae_cb14 | Consider the real-world 3D locations of the objects. Are the a man in a white shirt and the a man in a green shirt sitting next to each other or far away from each other? | next to each other | far away from each other | null | null | [
{
"bbox_3d": [
-0.4,
1.3,
2.9
],
"label": "a man in a white shirt"
},
{
"bbox_3d": [
-0.1,
2.6,
2.3
],
"label": "a man in a green shirt sitting"
}
] | [] | A | To solve this problem, we first estimate the 3D locations of a man in a white shirt and a man in a green shirt sitting. Then we can compute the L2 distance between the two objects. Next we estimate the rough sizes of the two objects. If the distance between the two objects is smaller or roughly the same as the object s... | A. next to each other. | location_next_to | 0006567209c36aae.jpg |
0007a2e7a0558219_6741 | Consider the real-world 3D location of the objects. Which object is closer to the camera? | a man in a suit | a bouquet of flowers | null | null | [
{
"bbox_3d": [
-0.3,
1.3,
3.6
],
"label": "a man in a suit"
},
{
"bbox_3d": [
0.1,
0.8,
2.6
],
"label": "a bouquet of flowers"
}
] | [] | B | To solve this problem, we first estimate the 3D locations of a man in a suit and a bouquet of flowers. Then we estimate the L2 distances from the camera to the two objects. The object with a smaller L2 distance is the one that is closer to the camera. The 3D location of a man in a suit is (-0.3, 1.3, 3.6). The 3D locat... | B. a bouquet of flowers. | location_closer_to_camera | 0007a2e7a0558219.jpg |
0007b54189a67423_2022 | Consider the real-world 3D locations of the objects. Is a blue key on an orange background directly underneath a book with keys on the cover? | yes | no | null | null | [
{
"bbox_3d": [
0,
0.7,
1.1
],
"label": "a book with keys on the cover"
},
{
"bbox_3d": [
0.2,
0.6,
1
],
"label": "a blue key on an orange background"
}
] | [] | B | To solve this problem, we first determine the 3D locations of a book with keys on the cover and a blue key on an orange background. Then we compute the vector pointing from a blue key on an orange background to a book with keys on the cover, as well as the up direction of a blue key on an orange background. We estimate... | B. no. | location_above | 0007b54189a67423.jpg |
000820195947013c_f7a0 | Consider the real-world 3D locations of the objects. Are the a baseball player wearing a white uniform and the a baseball player next to each other or far away from each other? | next to each other | far away from each other | null | null | [
{
"bbox_3d": [
1.8,
1.7,
20
],
"label": "a baseball player wearing a white uniform"
},
{
"bbox_3d": [
1.2,
2.1,
42.5
],
"label": "a baseball player"
}
] | [] | A | To solve this problem, we first estimate the 3D locations of a baseball player wearing a white uniform and a baseball player. Then we can compute the L2 distance between the two objects. Next we estimate the rough sizes of the two objects. If the distance between the two objects is smaller or roughly the same as the ob... | A. next to each other. | location_next_to | 000820195947013c.jpg |
00084a0cc3a13a3b_f2b0 | Consider the real-world 3D locations of the objects. Are the a woman in a white dress and the a man in a white hat next to each other or far away from each other? | next to each other | far away from each other | null | null | [
{
"bbox_3d": [
5.2,
1.9,
33.6
],
"label": "a woman in a white dress"
},
{
"bbox_3d": [
5.4,
1.1,
30.7
],
"label": "a man in a white hat"
}
] | [] | A | To solve this problem, we first estimate the 3D locations of a woman in a white dress and a man in a white hat. Then we can compute the L2 distance between the two objects. Next we estimate the rough sizes of the two objects. If the distance between the two objects is smaller or roughly the same as the object sizes, th... | A. next to each other. | location_next_to | 00084a0cc3a13a3b.jpg |
000857e1a9aee1ed_cf43 | Consider the real-world 3D orientations of the objects. What is the relationship between the orientations of a blue boat with a white bottom and a white boat floating in the water, parallel of perpendicular to each other? | parallel | perpendicular | null | null | [
{
"bbox_3d": [
-9.5,
3.8,
53.4
],
"label": "a blue boat with a white bottom"
},
{
"bbox_3d": [
-3.9,
4.1,
49.5
],
"label": "a white boat floating in the water"
}
] | [
{
"front_dir": [
0.2,
-0.1,
-1
],
"label": "a blue boat with a white bottom",
"left_dir": [
-1,
0,
-0.2
]
},
{
"front_dir": [
-1,
0,
-0.3
],
"label": "a white boat floating in the water",
"left_dir": [
-0.3,
0,... | B | To solve this problem, we first detect the front directions of a blue boat with a white bottom and a white boat floating in the water. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is closer to 0 or 180, than to 90 deg... | B. perpendicular. | multi_object_parallel | 000857e1a9aee1ed.jpg |
0008649d2c19d845_68b6 | Consider the real-world 3D locations of the objects. Which object has a higher location? | a white snowy surface | a long wooden plank | null | null | [
{
"bbox_3d": [
0.2,
0.4,
0.6
],
"label": "a white snowy surface"
},
{
"bbox_3d": [
-0.2,
0.5,
0.9
],
"label": "a long wooden plank"
}
] | [] | B | To solve this problem, we first estimate the 3D heights of the two objects. The object with a larger height value is at a higher location. The object with a smaller height value is at a lower location. The 3D height of a white snowy surface is 1.3. The 3D height of a long wooden plank is 1.8. The 3D height of a long wo... | B. a long wooden plank. | height_higher | 0008649d2c19d845.jpg |
00090aa6938b7de9_ba3f | Consider the real-world 3D locations and orientations of the objects. If I stand at a white comforter on a bed's position facing where it is facing, is a hand with a bandage on it in front of me or behind me? | in front of | behind | null | null | [
{
"bbox_3d": [
-0.2,
0.9,
0.5
],
"label": "a hand with a bandage on it"
},
{
"bbox_3d": [
-0.1,
0.5,
0.9
],
"label": "a white comforter on a bed"
}
] | [
{
"front_dir": [
0.1,
0.7,
-0.7
],
"label": "a white comforter on a bed",
"left_dir": [
-0.9,
0.3,
0.1
]
}
] | A | To solve this problem, we first determine the 3D locations of a hand with a bandage on it and a white comforter on a bed. Then we estimate the vector pointing from a white comforter on a bed to a hand with a bandage on it, as well as the front direction of a white comforter on a bed. Next we compute the cosine similari... | A. in front of. | orientation_in_front_of | 00090aa6938b7de9.jpg |
0009c51c7727cce0_ed2d | Consider the real-world 3D locations and orientations of the objects. Which side of a computer with a keyboard is facing the camera? | front | left | back | right | [
{
"bbox_3d": [
-0.1,
0,
0.6
],
"label": "a computer with a keyboard"
}
] | [
{
"front_dir": [
0.2,
0.1,
-1
],
"label": "a computer with a keyboard",
"left_dir": [
-1,
0.1,
-0.2
]
}
] | A | To solve this problem, we first estimate the 3D location of a computer with a keyboard. Then we obtain the vector pointing from the object to the camera. Now we compute the angles between the vector and the left, right, front, back directions. We first compute the left direction of a computer with a keyboard, which lea... | A. front. | orientation_viewpoint | 0009c51c7727cce0.jpg |
0009d6b2e2f0c698_e6f6 | Consider the real-world 3D locations and orientations of the objects. Which side of a stainless steel exhaust hood is facing a red onion with a white center? | front | left | back | right | [
{
"bbox_3d": [
2.5,
1.8,
7.9
],
"label": "a stainless steel exhaust hood"
},
{
"bbox_3d": [
0.3,
0.7,
1.2
],
"label": "a red onion with a white center"
}
] | [
{
"front_dir": [
-0.2,
-0.3,
-0.9
],
"label": "a stainless steel exhaust hood",
"left_dir": [
-1,
0,
0.2
]
}
] | A | To solve this problem, we first detect the 3D locations of a stainless steel exhaust hood and a red onion with a white center. Then we compute the vector pointing from a stainless steel exhaust hood to a red onion with a white center. Now we compute the angles between the vector and the left, right, front, back directi... | A. front. | multi_object_viewpoint_towards_object | 0009d6b2e2f0c698.jpg |
0009ddf40bd5258a_c8d3 | Consider the real-world 3D location of the objects. Which object is further away from the camera? | a bottle of lotion | a bottle with a blue label | null | null | [
{
"bbox_3d": [
0.9,
0.2,
3.8
],
"label": "a bottle of lotion"
},
{
"bbox_3d": [
-0.6,
1.1,
4.9
],
"label": "a bottle with a blue label"
}
] | [] | B | To solve this problem, we first estimate the 3D locations of a bottle of lotion and a bottle with a blue label. Then we estimate the L2 distances from the camera to the two objects. The object with a smaller L2 distance is the one that is closer to the camera. The 3D location of a bottle of lotion is (0.9, 0.2, 3.8). T... | B. a bottle with a blue label. | location_closer_to_camera | 0009ddf40bd5258a.jpg |
000a0945ecb24c23_ed12 | Consider the real-world 3D locations and orientations of the objects. If I stand at a white toilet bowl's position facing where it is facing, is a white toilet with a black seat on the left or right of me? | on the left | on the right | null | null | [
{
"bbox_3d": [
-0.1,
0.4,
0.2
],
"label": "a white toilet with a black seat"
},
{
"bbox_3d": [
0.4,
0.6,
0.4
],
"label": "a white toilet bowl"
}
] | [
{
"front_dir": [
-0.2,
0.5,
-0.8
],
"label": "a white toilet bowl",
"left_dir": [
-1,
-0.2,
0.2
]
}
] | A | To solve this problem, we first determine the 3D locations of a white toilet with a black seat and a white toilet bowl. Then we estimate the vector pointing from a white toilet bowl to a white toilet with a black seat, as well as the left direction of a white toilet bowl. Next we compute the cosine similarities between... | A. on the left. | orientation_on_the_left | 000a0945ecb24c23.jpg |
000a7b3789b1392e_aaec | Consider the real-world 3D locations of the objects. Which object has a lower location? | a red umbrella | a pink flower with green leaves | null | null | [
{
"bbox_3d": [
1,
5.9,
29
],
"label": "a red umbrella"
},
{
"bbox_3d": [
-10.1,
4.4,
23.7
],
"label": "a pink flower with green leaves"
}
] | [] | A | To solve this problem, we first estimate the 3D heights of the two objects. The object with a larger height value is at a higher location. The object with a smaller height value is at a lower location. The 3D height of a red umbrella is 6.2. The 3D height of a pink flower with green leaves is 14.9. The 3D height of a p... | A. a red umbrella | height_higher | 000a7b3789b1392e.jpg |
000a9b9566125270_19f6 | Consider the real-world 3D location of the objects. Which object is further away from the camera? | a green plant with a brown bug on it | a gray insect with long antennae | null | null | [
{
"bbox_3d": [
-0.2,
0.4,
1.4
],
"label": "a green plant with a brown bug on it"
},
{
"bbox_3d": [
0.4,
0.4,
1.1
],
"label": "a gray insect with long antennae"
}
] | [] | A | To solve this problem, we first estimate the 3D locations of a green plant with a brown bug on it and a gray insect with long antennae. Then we estimate the L2 distances from the camera to the two objects. The object with a smaller L2 distance is the one that is closer to the camera. The 3D location of a green plant wi... | A. a green plant with a brown bug on it. | location_closer_to_camera | 000a9b9566125270.jpg |
000b05b7f49cdacd_f56b | Consider the real-world 3D locations of the objects. Which object has a higher location? | a rocky cliff with a cave | a man in a black jacket walking | null | null | [
{
"bbox_3d": [
-2.4,
2.8,
27.1
],
"label": "a rocky cliff with a cave"
},
{
"bbox_3d": [
1.8,
0.9,
19.2
],
"label": "a man in a black jacket walking"
}
] | [] | A | To solve this problem, we first estimate the 3D heights of the two objects. The object with a larger height value is at a higher location. The object with a smaller height value is at a lower location. The 3D height of a rocky cliff with a cave is 7.4. The 3D height of a man in a black jacket walking is 2.1. The 3D hei... | A. a man in a black jacket walking. | height_higher | 000b05b7f49cdacd.jpg |
000b4671075914cd_e216 | Consider the real-world 3D locations and orientations of the objects. If I stand at a computer monitor's position facing where it is facing, is a black microphone with a silver ring around it in front of me or behind me? | in front of | behind | null | null | [
{
"bbox_3d": [
0.1,
0.8,
1.6
],
"label": "a black microphone with a silver ring around it"
},
{
"bbox_3d": [
1.6,
0.7,
4.8
],
"label": "a computer monitor"
}
] | [
{
"front_dir": [
0.2,
-0.2,
-1
],
"label": "a computer monitor",
"left_dir": [
-1,
0,
-0.2
]
}
] | A | To solve this problem, we first determine the 3D locations of a black microphone with a silver ring around it and a computer monitor. Then we estimate the vector pointing from a computer monitor to a black microphone with a silver ring around it, as well as the front direction of a computer monitor. Next we compute the... | A. in front of. | orientation_in_front_of | 000b4671075914cd.jpg |
000bb2f7132013dc_adc0 | Consider the real-world 3D locations of the objects. Which object has a lower location? | a tree with white flowers | a hill with a steep slope | null | null | [
{
"bbox_3d": [
0.4,
1.9,
51.3
],
"label": "a tree with white flowers"
},
{
"bbox_3d": [
16.9,
20.8,
173.7
],
"label": "a hill with a steep slope"
}
] | [] | A | To solve this problem, we first estimate the 3D heights of the two objects. The object with a larger height value is at a higher location. The object with a smaller height value is at a lower location. The 3D height of a tree with white flowers is 6.1. The 3D height of a hill with a steep slope is 48.0. The 3D height o... | A. a tree with white flowers | height_higher | 000bb2f7132013dc.jpg |
000bcee5bed5446b_f55f | Consider the real-world 3D orientations of the objects. What is the relationship between the orientations of a black car with a white stripe on the back and a car with a white roof, parallel of perpendicular to each other? | parallel | perpendicular | null | null | [
{
"bbox_3d": [
-5.7,
4.6,
91.8
],
"label": "a black car with a white stripe on the back"
},
{
"bbox_3d": [
-4.5,
3.1,
80.9
],
"label": "a car with a white roof"
}
] | [
{
"front_dir": [
0.1,
0,
-1
],
"label": "a black car with a white stripe on the back",
"left_dir": [
-1,
0.1,
-0.1
]
},
{
"front_dir": [
0.1,
-0.1,
-1
],
"label": "a car with a white roof",
"left_dir": [
-1,
0.... | A | To solve this problem, we first detect the front directions of a black car with a white stripe on the back and a car with a white roof. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is closer to 0 or 180, than to 90 de... | A. parallel. | multi_object_parallel | 000bcee5bed5446b.jpg |
000c5efb8f0d938f_902e | Consider the real-world 3D locations and orientations of the objects. Which side of a bulletin board with a coca cola advertisement is facing a pile of oranges and kiwis? | front | left | back | right | [
{
"bbox_3d": [
0.3,
1,
5.4
],
"label": "a bulletin board with a coca cola advertisement"
},
{
"bbox_3d": [
-0.7,
0.9,
1.6
],
"label": "a pile of oranges and kiwis"
}
] | [
{
"front_dir": [
0,
0,
-1
],
"label": "a bulletin board with a coca cola advertisement",
"left_dir": [
-1,
0.1,
0
]
}
] | A | To solve this problem, we first detect the 3D locations of a bulletin board with a coca cola advertisement and a pile of oranges and kiwis. Then we compute the vector pointing from a bulletin board with a coca cola advertisement to a pile of oranges and kiwis. Now we compute the angles between the vector and the left, ... | A. front. | multi_object_viewpoint_towards_object | 000c5efb8f0d938f.jpg |
000cca22af11a3d9_6b1a | Consider the real-world 3D location of the objects. Which object is further away from the camera? | a black curtain | a wooden floor | null | null | [
{
"bbox_3d": [
-1.4,
2.3,
7.9
],
"label": "a black curtain"
},
{
"bbox_3d": [
0.9,
0.1,
4.2
],
"label": "a wooden floor"
}
] | [] | A | To solve this problem, we first estimate the 3D locations of a black curtain and a wooden floor. Then we estimate the L2 distances from the camera to the two objects. The object with a smaller L2 distance is the one that is closer to the camera. The 3D location of a black curtain is (-1.4, 2.3, 7.9). The 3D location of... | A. a black curtain. | location_closer_to_camera | 000cca22af11a3d9.jpg |
000cd0b046e4390b_0311 | Consider the real-world 3D orientations of the objects. What is the relationship between the orientations of a car on the road and a car on the road, parallel of perpendicular to each other? | parallel | perpendicular | null | null | [
{
"bbox_3d": [
0.6,
1.2,
1.2
],
"label": "a car on the road"
},
{
"bbox_3d": [
0.6,
1,
1.1
],
"label": "a car on the road"
}
] | [
{
"front_dir": [
-0.4,
0.1,
-0.9
],
"label": "a car on the road",
"left_dir": [
-0.9,
0,
0.4
]
},
{
"front_dir": [
-0.4,
0,
-0.9
],
"label": "a car on the road",
"left_dir": [
-0.9,
0,
0.4
]
}
] | A | To solve this problem, we first detect the front directions of a car on the road and a car on the road. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is closer to 0 or 180, than to 90 degrees, then the two objects are ... | A. parallel. | multi_object_parallel | 000cd0b046e4390b.jpg |
000d608653d67333_1ab0 | Consider the real-world 3D locations and orientations of the objects. Which side of a black chair with a glass table is facing a table with a brown top? | front | left | back | right | [
{
"bbox_3d": [
-0.5,
1,
1.6
],
"label": "a black chair with a glass table"
},
{
"bbox_3d": [
-1.1,
0.6,
4.3
],
"label": "a table with a brown top"
}
] | [
{
"front_dir": [
0.4,
0.3,
-0.9
],
"label": "a black chair with a glass table",
"left_dir": [
-0.9,
0.1,
-0.3
]
}
] | C | To solve this problem, we first detect the 3D locations of a black chair with a glass table and a table with a brown top. Then we compute the vector pointing from a black chair with a glass table to a table with a brown top. Now we compute the angles between the vector and the left, right, front, back directions. We fi... | C. back. | multi_object_viewpoint_towards_object | 000d608653d67333.jpg |
000e082b0dce859e_edfe | Consider the real-world 3D locations and orientations of the objects. If I stand at a wooden stool with a metal brace's position facing where it is facing, is a wooden table with a cutting board on it in front of me or behind me? | in front of | behind | null | null | [
{
"bbox_3d": [
-1.2,
0.9,
3.3
],
"label": "a wooden table with a cutting board on it"
},
{
"bbox_3d": [
0,
0.3,
1.3
],
"label": "a wooden stool with a metal brace"
}
] | [
{
"front_dir": [
0.8,
0.1,
-0.5
],
"label": "a wooden stool with a metal brace",
"left_dir": [
-0.6,
0.2,
-0.8
]
}
] | B | To solve this problem, we first determine the 3D locations of a wooden table with a cutting board on it and a wooden stool with a metal brace. Then we estimate the vector pointing from a wooden stool with a metal brace to a wooden table with a cutting board on it, as well as the front direction of a wooden stool with a... | B. behind. | orientation_in_front_of | 000e082b0dce859e.jpg |
000e70d6b9f25c3d_815e | Consider the real-world 3D orientations of the objects. Are a white couch with a brown pillow and a fireplace with a black metal grate and a brick wall facing same or similar directions, or very different directions? | same or similar directions | very different directions | null | null | [
{
"bbox_3d": [
-1.2,
0.5,
4.1
],
"label": "a white couch with a brown pillow"
},
{
"bbox_3d": [
0.2,
0.6,
6.3
],
"label": "a fireplace with a black metal grate and a brick wall"
}
] | [
{
"front_dir": [
0.2,
0,
-1
],
"label": "a white couch with a brown pillow",
"left_dir": [
-1,
-0.1,
-0.2
]
},
{
"front_dir": [
-0.1,
0.1,
-1
],
"label": "a fireplace with a black metal grate and a brick wall",
"left_dir":... | A | To solve this problem, we first detect the front directions of a white couch with a brown pillow and a fireplace with a black metal grate and a brick wall. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is small, then t... | A. same or similar directions. | multi_object_same_direction | 000e70d6b9f25c3d.jpg |
000e842c55ab7d14_5e6c | Consider the real-world 3D locations and orientations of the objects. Which side of a red sports car is facing the camera? | front | left | back | right | [
{
"bbox_3d": [
3.3,
0.8,
5.7
],
"label": "a red sports car"
}
] | [
{
"front_dir": [
0.9,
0.1,
-0.4
],
"label": "a red sports car",
"left_dir": [
-0.4,
-0.1,
-0.9
]
}
] | B | To solve this problem, we first estimate the 3D location of a red sports car. Then we obtain the vector pointing from the object to the camera. Now we compute the angles between the vector and the left, right, front, back directions. We first compute the left direction of a red sports car, which leads to the angle betw... | B. left. | orientation_viewpoint | 000e842c55ab7d14.jpg |
000edafa589fc672_8c57 | Consider the real-world 3D locations and orientations of the objects. Which side of a metal chair with a wooden seat is facing the camera? | front | left | back | right | [
{
"bbox_3d": [
4.4,
0.7,
8.9
],
"label": "a metal chair with a wooden seat"
}
] | [
{
"front_dir": [
0.9,
0,
-0.5
],
"label": "a metal chair with a wooden seat",
"left_dir": [
-0.5,
0.2,
-0.8
]
}
] | B | To solve this problem, we first estimate the 3D location of a metal chair with a wooden seat. Then we obtain the vector pointing from the object to the camera. Now we compute the angles between the vector and the left, right, front, back directions. We first compute the left direction of a metal chair with a wooden sea... | B. left. | orientation_viewpoint | 000edafa589fc672.jpg |
000edafa589fc672_993f | Consider the real-world 3D orientations of the objects. What is the relationship between the orientations of a chair with a white seat and a red chair with a metal frame, parallel of perpendicular to each other? | parallel | perpendicular | null | null | [
{
"bbox_3d": [
1.7,
1.3,
3.4
],
"label": "a chair with a white seat"
},
{
"bbox_3d": [
0.2,
0.5,
8
],
"label": "a red chair with a metal frame"
}
] | [
{
"front_dir": [
-0.8,
0.2,
-0.5
],
"label": "a chair with a white seat",
"left_dir": [
-0.5,
-0.1,
0.8
]
},
{
"front_dir": [
0,
-0.3,
-0.9
],
"label": "a red chair with a metal frame",
"left_dir": [
-1,
0.1,
... | B | To solve this problem, we first detect the front directions of a chair with a white seat and a red chair with a metal frame. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is closer to 0 or 180, than to 90 degrees, then... | B. perpendicular. | multi_object_parallel | 000edafa589fc672.jpg |
000edafa589fc672_0e8a | Consider the real-world 3D orientations of the objects. Are a metal chair with a red seat and a red chair with a white jacket draped over it facing same or similar directions, or very different directions? | same or similar directions | very different directions | null | null | [
{
"bbox_3d": [
0.8,
0.2,
9.2
],
"label": "a metal chair with a red seat"
},
{
"bbox_3d": [
1.1,
1,
2.9
],
"label": "a red chair with a white jacket draped over it"
}
] | [
{
"front_dir": [
-1,
0.1,
0
],
"label": "a metal chair with a red seat",
"left_dir": [
0,
0,
1
]
},
{
"front_dir": [
-0.3,
0,
-1
],
"label": "a red chair with a white jacket draped over it",
"left_dir": [
-1,
0... | B | To solve this problem, we first detect the front directions of a metal chair with a red seat and a red chair with a white jacket draped over it. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is small, then the two obje... | B. very different directions. | multi_object_same_direction | 000edafa589fc672.jpg |
000f63dc614130ae_e16a | Consider the real-world 3D locations and orientations of the objects. Which side of a chair with a man sitting in it is facing a brown chair? | front | left | back | right | [
{
"bbox_3d": [
-0.4,
0.6,
1.6
],
"label": "a chair with a man sitting in it"
},
{
"bbox_3d": [
1.1,
0.4,
1.9
],
"label": "a brown chair"
}
] | [
{
"front_dir": [
1,
-0.2,
0.2
],
"label": "a chair with a man sitting in it",
"left_dir": [
0,
-0.6,
-0.8
]
},
{
"front_dir": [
0,
0.1,
-1
],
"label": "a brown chair",
"left_dir": [
-1,
-0.1,
-0.1
]
... | A | To solve this problem, we first detect the 3D locations of a chair with a man sitting in it and a brown chair. Then we compute the vector pointing from a chair with a man sitting in it to a brown chair. Now we compute the angles between the vector and the left, right, front, back directions. We first compute the left d... | A. front. | multi_object_viewpoint_towards_object | 000f63dc614130ae.jpg |
000f96aacadf7aa5_14cd | Consider the real-world 3D location of the objects. Which object is closer to the camera? | a white jeep with luggage on top | a dirt field with a tractor | null | null | [
{
"bbox_3d": [
-3.5,
2.4,
10.7
],
"label": "a white jeep with luggage on top"
},
{
"bbox_3d": [
-3.2,
0.1,
5.8
],
"label": "a dirt field with a tractor"
}
] | [] | B | To solve this problem, we first estimate the 3D locations of a white jeep with luggage on top and a dirt field with a tractor. Then we estimate the L2 distances from the camera to the two objects. The object with a smaller L2 distance is the one that is closer to the camera. The 3D location of a white jeep with luggage... | B. a dirt field with a tractor. | location_closer_to_camera | 000f96aacadf7aa5.jpg |
000fbfa6ec5b6457_dc19 | Consider the real-world 3D locations of the objects. Are the a green bottle with a white label and the a woman in a black shirt looking at her phone next to each other or far away from each other? | next to each other | far away from each other | null | null | [
{
"bbox_3d": [
0,
0.9,
1.5
],
"label": "a green bottle with a white label"
},
{
"bbox_3d": [
0.1,
1,
2.8
],
"label": "a woman in a black shirt looking at her phone"
}
] | [] | B | To solve this problem, we first estimate the 3D locations of a green bottle with a white label and a woman in a black shirt looking at her phone. Then we can compute the L2 distance between the two objects. Next we estimate the rough sizes of the two objects. If the distance between the two objects is smaller or roughl... | B. far away from each other. | location_next_to | 000fbfa6ec5b6457.jpg |
000fd820b7972143_6699 | Consider the real-world 3D locations of the objects. Is a man in a black suit directly underneath a hand with a red tie? | yes | no | null | null | [
{
"bbox_3d": [
0.3,
0.8,
2.1
],
"label": "a hand with a red tie"
},
{
"bbox_3d": [
0.3,
0.6,
2.2
],
"label": "a man in a black suit"
}
] | [] | B | To solve this problem, we first determine the 3D locations of a hand with a red tie and a man in a black suit. Then we compute the vector pointing from a man in a black suit to a hand with a red tie, as well as the up direction of a man in a black suit. We estimate the cosine similarity between the vector and the up di... | B. no. | location_above | 000fd820b7972143.jpg |
000fe11025f2e246_6133 | Consider the real-world 3D locations and orientations of the objects. Which object is a dog with a long tail facing towards, a woman wearing a green shirt or the a busy street with people riding motorcycles? | a woman wearing a green shirt | a busy street with people riding motorcycles | null | null | [
{
"bbox_3d": [
-6.4,
1,
30.6
],
"label": "a dog with a long tail"
},
{
"bbox_3d": [
0.1,
1.1,
7.3
],
"label": "a woman wearing a green shirt"
},
{
"bbox_3d": [
0.3,
5.9,
31.4
],
"label": "a busy street with people ridi... | [
{
"front_dir": [
0.3,
0,
-1
],
"label": "a dog with a long tail",
"left_dir": [
-1,
0,
-0.3
]
}
] | A | To solve this problem, we first detect the 3D location of a dog with a long tail, a woman wearing a green shirt, and a busy street with people riding motorcycles. Then we compute the cosine similarities between the front direction of a dog with a long tail and the vectors from a dog with a long tail to the other two ob... | A. a woman wearing a green shirt. | multi_object_facing | 000fe11025f2e246.jpg |
000fe628fd1fa9d2_e977 | Consider the real-world 3D locations and orientations of the objects. Which side of a white van is driving on the road is facing the camera? | front | left | back | right | [
{
"bbox_3d": [
-0.4,
2.1,
16.3
],
"label": "a white van is driving on the road"
}
] | [
{
"front_dir": [
-0.1,
0.2,
1
],
"label": "a white van is driving on the road",
"left_dir": [
1,
-0.1,
0.1
]
}
] | C | To solve this problem, we first estimate the 3D location of a white van is driving on the road. Then we obtain the vector pointing from the object to the camera. Now we compute the angles between the vector and the left, right, front, back directions. We first compute the left direction of a white van is driving on the... | C. back. | orientation_viewpoint | 000fe628fd1fa9d2.jpg |
001021cac2f74637_b685 | Consider the real-world 3D locations and orientations of the objects. If I stand at a stool with a brown wooden top's position facing where it is facing, is a white sock with a yellow stripe on the left or right of me? | on the left | on the right | null | null | [
{
"bbox_3d": [
0.3,
-0.1,
1.3
],
"label": "a white sock with a yellow stripe"
},
{
"bbox_3d": [
-0.5,
-0.1,
2
],
"label": "a stool with a brown wooden top"
}
] | [
{
"front_dir": [
0,
0.8,
0.6
],
"label": "a stool with a brown wooden top",
"left_dir": [
0.3,
0.6,
-0.8
]
}
] | A | To solve this problem, we first determine the 3D locations of a white sock with a yellow stripe and a stool with a brown wooden top. Then we estimate the vector pointing from a stool with a brown wooden top to a white sock with a yellow stripe, as well as the left direction of a stool with a brown wooden top. Next we c... | A. on the left. | orientation_on_the_left | 001021cac2f74637.jpg |
0010492df4e1b810_c9d8 | Consider the real-world 3D locations and orientations of the objects. Which object is a blue stool facing towards, a black tablecloth on a table or the a black camera with a yellow cord? | a black tablecloth on a table | a black camera with a yellow cord | null | null | [
{
"bbox_3d": [
0.3,
1,
2.4
],
"label": "a blue stool"
},
{
"bbox_3d": [
3.6,
0.8,
6.6
],
"label": "a black tablecloth on a table"
},
{
"bbox_3d": [
0.9,
1.1,
2.1
],
"label": "a black camera with a yellow cord"
}
] | [
{
"front_dir": [
1,
0,
-0.2
],
"label": "a blue stool",
"left_dir": [
-0.2,
0.1,
-1
]
},
{
"front_dir": [
1,
0,
-0.3
],
"label": "a black camera with a yellow cord",
"left_dir": [
-0.3,
0.3,
-0.9
]
... | B | To solve this problem, we first detect the 3D location of a blue stool, a black tablecloth on a table, and a black camera with a yellow cord. Then we compute the cosine similarities between the front direction of a blue stool and the vectors from a blue stool to the other two objects. We can estimate the angles from th... | B. a black camera with a yellow cord. | multi_object_facing | 0010492df4e1b810.jpg |
0010f4c10f7ab07e_04d4 | Consider the real-world 3D orientations of the objects. Are a silver car parked on the street and a brown car facing same or similar directions, or very different directions? | same or similar directions | very different directions | null | null | [
{
"bbox_3d": [
3,
0.9,
6.2
],
"label": "a silver car parked on the street"
},
{
"bbox_3d": [
12.4,
0.4,
32.3
],
"label": "a brown car"
}
] | [
{
"front_dir": [
-0.4,
0,
-0.9
],
"label": "a silver car parked on the street",
"left_dir": [
-0.9,
0,
0.4
]
},
{
"front_dir": [
0,
-0.1,
-1
],
"label": "a brown car",
"left_dir": [
-1,
0,
0
]
}
] | A | To solve this problem, we first detect the front directions of a silver car parked on the street and a brown car. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is small, then the two objects are facing same or similar ... | A. same or similar directions. | multi_object_same_direction | 0010f4c10f7ab07e.jpg |
00119d989d1e515e_01e9 | Consider the real-world 3D orientations of the objects. What is the relationship between the orientations of a red car with a black interior and a silver car, parallel of perpendicular to each other? | parallel | perpendicular | null | null | [
{
"bbox_3d": [
1.6,
1.2,
6.5
],
"label": "a red car with a black interior"
},
{
"bbox_3d": [
0.9,
1.7,
7
],
"label": "a silver car"
}
] | [
{
"front_dir": [
-0.2,
0,
-1
],
"label": "a red car with a black interior",
"left_dir": [
-1,
0.1,
0.2
]
},
{
"front_dir": [
-0.1,
-0.1,
-1
],
"label": "a silver car",
"left_dir": [
-1,
0.1,
0
]
}
] | A | To solve this problem, we first detect the front directions of a red car with a black interior and a silver car. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is closer to 0 or 180, than to 90 degrees, then the two obj... | A. parallel. | multi_object_parallel | 00119d989d1e515e.jpg |
0011ace1c64f7919_4f8f | Consider the real-world 3D locations of the objects. Which is closer to a cartoon of a goose, a canopy with lights on it or a calm lake with a few trees? | a canopy with lights on it | a calm lake with a few trees | null | null | [
{
"bbox_3d": [
-9.4,
1.1,
23.2
],
"label": "a cartoon of a goose"
},
{
"bbox_3d": [
1.4,
2.1,
6.5
],
"label": "a canopy with lights on it"
},
{
"bbox_3d": [
-8.4,
3.3,
58
],
"label": "a calm lake with a few trees"
}
... | [] | A | To solve this problem, we first detect the 3D location of a cartoon of a goose, a canopy with lights on it, and a calm lake with a few trees. Then we compute the L2 distances between a cartoon of a goose and a canopy with lights on it, and between a cartoon of a goose and a calm lake with a few trees. The object that i... | A. a canopy with lights on it. | multi_object_closer_to | 0011ace1c64f7919.jpg |
0011cf9a929a4e19_ac89 | Consider the real-world 3D orientations of the objects. Are a large stone building with a black roof and a white motorbike with a black leather seat facing same or similar directions, or very different directions? | same or similar directions | very different directions | null | null | [
{
"bbox_3d": [
-0.7,
12,
38.6
],
"label": "a large stone building with a black roof"
},
{
"bbox_3d": [
0.4,
0.9,
17.8
],
"label": "a white motorbike with a black leather seat"
}
] | [
{
"front_dir": [
-0.4,
-0.3,
-0.9
],
"label": "a large stone building with a black roof",
"left_dir": [
-0.9,
0.2,
0.3
]
},
{
"front_dir": [
-1,
0.1,
-0.1
],
"label": "a white motorbike with a black leather seat",
"left_di... | B | To solve this problem, we first detect the front directions of a large stone building with a black roof and a white motorbike with a black leather seat. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is small, then the ... | B. very different directions. | multi_object_same_direction | 0011cf9a929a4e19.jpg |
0011cf9a929a4e19_2788 | Consider the real-world 3D orientations of the objects. Are a red car and a motorbike with a clear windshield facing same or similar directions, or very different directions? | same or similar directions | very different directions | null | null | [
{
"bbox_3d": [
0.9,
1,
4.8
],
"label": "a red car"
},
{
"bbox_3d": [
-4.4,
0.5,
10.4
],
"label": "a motorbike with a clear windshield"
}
] | [
{
"front_dir": [
-1,
0.1,
0.1
],
"label": "a red car",
"left_dir": [
0.1,
0,
1
]
},
{
"front_dir": [
0.5,
0,
-0.9
],
"label": "a motorbike with a clear windshield",
"left_dir": [
-0.9,
0,
-0.5
]
}
] | B | To solve this problem, we first detect the front directions of a red car and a motorbike with a clear windshield. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is small, then the two objects are facing same or similar ... | B. very different directions. | multi_object_same_direction | 0011cf9a929a4e19.jpg |
0011e949e5712f18_626f | Consider the real-world 3D locations and orientations of the objects. If I stand at a wooden bookshelf with a green box on top's position facing where it is facing, is a plastic binder with books on the left or right of me? | on the left | on the right | null | null | [
{
"bbox_3d": [
-0.2,
0.8,
0.6
],
"label": "a plastic binder with books"
},
{
"bbox_3d": [
0.3,
0.7,
0.5
],
"label": "a wooden bookshelf with a green box on top"
}
] | [
{
"front_dir": [
-0.3,
0.6,
-0.7
],
"label": "a wooden bookshelf with a green box on top",
"left_dir": [
-0.9,
0,
0.3
]
}
] | A | To solve this problem, we first determine the 3D locations of a plastic binder with books and a wooden bookshelf with a green box on top. Then we estimate the vector pointing from a wooden bookshelf with a green box on top to a plastic binder with books, as well as the left direction of a wooden bookshelf with a green ... | A. on the left. | orientation_on_the_left | 0011e949e5712f18.jpg |
00123a36def39bf4_59e3 | Consider the real-world 3D locations and orientations of the objects. Which side of a bicycle with a black frame and a silver seat is facing a man in a white shirt and yellow shorts playing a video game? | front | left | back | right | [
{
"bbox_3d": [
1.6,
0.6,
5.4
],
"label": "a bicycle with a black frame and a silver seat"
},
{
"bbox_3d": [
-6.2,
0.8,
12.5
],
"label": "a man in a white shirt and yellow shorts playing a video game"
}
] | [
{
"front_dir": [
0.7,
-0.2,
-0.7
],
"label": "a bicycle with a black frame and a silver seat",
"left_dir": [
-0.8,
-0.3,
-0.6
]
}
] | C | To solve this problem, we first detect the 3D locations of a bicycle with a black frame and a silver seat and a man in a white shirt and yellow shorts playing a video game. Then we compute the vector pointing from a bicycle with a black frame and a silver seat to a man in a white shirt and yellow shorts playing a video... | C. back. | multi_object_viewpoint_towards_object | 00123a36def39bf4.jpg |
001275e2a5ce462a_c32a | Consider the real-world 3D orientations of the objects. What is the relationship between the orientations of a brown leather chair and a black chair with a wooden frame, parallel of perpendicular to each other? | parallel | perpendicular | null | null | [
{
"bbox_3d": [
2.7,
0.7,
5.9
],
"label": "a brown leather chair"
},
{
"bbox_3d": [
2.9,
0.7,
8.8
],
"label": "a black chair with a wooden frame"
}
] | [
{
"front_dir": [
-0.4,
-0.1,
-0.9
],
"label": "a brown leather chair",
"left_dir": [
-0.9,
0.1,
0.3
]
},
{
"front_dir": [
-0.3,
-0.2,
-0.9
],
"label": "a black chair with a wooden frame",
"left_dir": [
-1,
0.1,... | A | To solve this problem, we first detect the front directions of a brown leather chair and a black chair with a wooden frame. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is closer to 0 or 180, than to 90 degrees, then ... | A. parallel. | multi_object_parallel | 001275e2a5ce462a.jpg |
00129ccb99612727_e0a9 | Consider the real-world 3D locations of the objects. Are the a cat with brown fur and the a carpeted floor next to each other or far away from each other? | next to each other | far away from each other | null | null | [
{
"bbox_3d": [
0.2,
0.2,
0.7
],
"label": "a cat with brown fur"
},
{
"bbox_3d": [
0,
0.1,
0.5
],
"label": "a carpeted floor"
}
] | [] | A | To solve this problem, we first estimate the 3D locations of a cat with brown fur and a carpeted floor. Then we can compute the L2 distance between the two objects. Next we estimate the rough sizes of the two objects. If the distance between the two objects is smaller or roughly the same as the object sizes, then the t... | A. next to each other. | location_next_to | 00129ccb99612727.jpg |
001334773bd8d5c8_27ec | Consider the real-world 3D locations of the objects. Which object has a higher location? | a car with a black roof | a horse drawn carriage | null | null | [
{
"bbox_3d": [
1.4,
7.1,
79.5
],
"label": "a car with a black roof"
},
{
"bbox_3d": [
-3.3,
1,
11.8
],
"label": "a horse drawn carriage"
}
] | [] | A | To solve this problem, we first estimate the 3D heights of the two objects. The object with a larger height value is at a higher location. The object with a smaller height value is at a lower location. The 3D height of a car with a black roof is 11.4. The 3D height of a horse drawn carriage is 2.3. The 3D height of a c... | A. a horse drawn carriage. | height_higher | 001334773bd8d5c8.jpg |
00145feb6b15d975_1b47 | Consider the real-world 3D locations of the objects. Which object has a higher location? | a white sandal | a brick sidewalk | null | null | [
{
"bbox_3d": [
0.1,
0.1,
3.2
],
"label": "a white sandal"
},
{
"bbox_3d": [
1,
0.1,
9.1
],
"label": "a brick sidewalk"
}
] | [] | B | To solve this problem, we first estimate the 3D heights of the two objects. The object with a larger height value is at a higher location. The object with a smaller height value is at a lower location. The 3D height of a white sandal is 0.3. The 3D height of a brick sidewalk is 0.6. The 3D height of a brick sidewalk is... | B. a brick sidewalk. | height_higher | 00145feb6b15d975.jpg |
0014f79a71bd5e17_440c | Consider the real-world 3D locations and orientations of the objects. If I stand at a white plastic chair's position facing where it is facing, is a white plastic chair on the left or right of me? | on the left | on the right | null | null | [
{
"bbox_3d": [
-3.2,
1.2,
6.4
],
"label": "a white plastic chair"
},
{
"bbox_3d": [
3.4,
1.5,
6.8
],
"label": "a white plastic chair"
}
] | [
{
"front_dir": [
1,
-0.2,
0.1
],
"label": "a white plastic chair",
"left_dir": [
0.1,
0,
-1
]
},
{
"front_dir": [
-0.4,
0.2,
-0.9
],
"label": "a white plastic chair",
"left_dir": [
-0.9,
0,
0.4
]
}
... | A | To solve this problem, we first determine the 3D locations of a white plastic chair and a white plastic chair. Then we estimate the vector pointing from a white plastic chair to a white plastic chair, as well as the left direction of a white plastic chair. Next we compute the cosine similarities between the vector and ... | A. on the left. | orientation_on_the_left | 0014f79a71bd5e17.jpg |
001503233549730c_021f | Consider the real-world 3D orientations of the objects. What is the relationship between the orientations of a black bicycle with a person riding it and a black bicycle with a white light, parallel of perpendicular to each other? | parallel | perpendicular | null | null | [
{
"bbox_3d": [
3.7,
-0.6,
24.7
],
"label": "a black bicycle with a person riding it"
},
{
"bbox_3d": [
2.6,
-0.7,
26.9
],
"label": "a black bicycle with a white light"
}
] | [
{
"front_dir": [
-0.1,
0,
-1
],
"label": "a black bicycle with a person riding it",
"left_dir": [
-1,
0.1,
0.1
]
},
{
"front_dir": [
0,
0,
-1
],
"label": "a black bicycle with a white light",
"left_dir": [
-1,
... | A | To solve this problem, we first detect the front directions of a black bicycle with a person riding it and a black bicycle with a white light. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is closer to 0 or 180, than t... | A. parallel. | multi_object_parallel | 001503233549730c.jpg |
001571ccc331d6b9_b053 | Consider the real-world 3D location of the objects. Which object is further away from the camera? | a stuffed animal hanging from a mirror | a grey car with red wheels | null | null | [
{
"bbox_3d": [
-0.2,
1.8,
10.4
],
"label": "a stuffed animal hanging from a mirror"
},
{
"bbox_3d": [
-0.5,
1,
4.9
],
"label": "a grey car with red wheels"
}
] | [] | A | To solve this problem, we first estimate the 3D locations of a stuffed animal hanging from a mirror and a grey car with red wheels. Then we estimate the L2 distances from the camera to the two objects. The object with a smaller L2 distance is the one that is closer to the camera. The 3D location of a stuffed animal han... | A. a stuffed animal hanging from a mirror. | location_closer_to_camera | 001571ccc331d6b9.jpg |
00166bc33fa71d0a_e667 | Consider the real-world 3D locations of the objects. Which object has a higher location? | a woman wearing a blue shirt | a red cup with a white rim | null | null | [
{
"bbox_3d": [
0.6,
1,
2.6
],
"label": "a woman wearing a blue shirt"
},
{
"bbox_3d": [
-0.3,
1,
2.2
],
"label": "a red cup with a white rim"
}
] | [] | A | To solve this problem, we first estimate the 3D heights of the two objects. The object with a larger height value is at a higher location. The object with a smaller height value is at a lower location. The 3D height of a woman wearing a blue shirt is 2.4. The 3D height of a red cup with a white rim is 1.1. The 3D heigh... | A. a red cup with a white rim. | height_higher | 00166bc33fa71d0a.jpg |
0016945112a29ba5_ee56 | Consider the real-world 3D locations of the objects. Is a tall tower with a green light directly above a tall building with lights on it? | yes | no | null | null | [
{
"bbox_3d": [
9.9,
41.1,
72.1
],
"label": "a tall tower with a green light"
},
{
"bbox_3d": [
5.6,
17.1,
58.1
],
"label": "a tall building with lights on it"
}
] | [] | A | To solve this problem, we first determine the 3D locations of a tall tower with a green light and a tall building with lights on it. Then we compute the vector pointing from a tall building with lights on it to a tall tower with a green light, as well as the up direction of a tall building with lights on it. We estimat... | A. yes. | location_above | 0016945112a29ba5.jpg |
0016c8a3e03153b6_e8b5 | Consider the real-world 3D locations and orientations of the objects. If I stand at a black wheelchair's position facing where it is facing, is a white pillar on the left or right of me? | on the left | on the right | null | null | [
{
"bbox_3d": [
0.7,
0.7,
2.9
],
"label": "a white pillar"
},
{
"bbox_3d": [
-0.5,
0.4,
1.5
],
"label": "a black wheelchair"
}
] | [
{
"front_dir": [
0.7,
0.2,
-0.7
],
"label": "a black wheelchair",
"left_dir": [
-0.7,
0.3,
-0.7
]
}
] | B | To solve this problem, we first determine the 3D locations of a white pillar and a black wheelchair. Then we estimate the vector pointing from a black wheelchair to a white pillar, as well as the left direction of a black wheelchair. Next we compute the cosine similarities between the vector and the left direction, whi... | B. on the right. | orientation_on_the_left | 0016c8a3e03153b6.jpg |
00170e17d0da613f_b4cf | Consider the real-world 3D location of the objects. Which object is closer to the camera? | a biscuit with a hole in the middle | a biscuit with black and white frosting | null | null | [
{
"bbox_3d": [
0.3,
0.1,
0.5
],
"label": "a biscuit with a hole in the middle"
},
{
"bbox_3d": [
0.1,
0.1,
0.4
],
"label": "a biscuit with black and white frosting"
}
] | [] | B | To solve this problem, we first estimate the 3D locations of a biscuit with a hole in the middle and a biscuit with black and white frosting. Then we estimate the L2 distances from the camera to the two objects. The object with a smaller L2 distance is the one that is closer to the camera. The 3D location of a biscuit ... | B. a biscuit with black and white frosting. | location_closer_to_camera | 00170e17d0da613f.jpg |
0017ccd0b1865cb8_626e | Consider the real-world 3D locations and orientations of the objects. Which side of a black podium is facing a person with brown hair? | front | left | back | right | [
{
"bbox_3d": [
0.4,
1.5,
15.7
],
"label": "a black podium"
},
{
"bbox_3d": [
-0.8,
0.3,
7.5
],
"label": "a person with brown hair"
}
] | [
{
"front_dir": [
0,
-0.3,
-0.9
],
"label": "a black podium",
"left_dir": [
-1,
0.1,
-0.1
]
}
] | A | To solve this problem, we first detect the 3D locations of a black podium and a person with brown hair. Then we compute the vector pointing from a black podium to a person with brown hair. Now we compute the angles between the vector and the left, right, front, back directions. We first compute the left direction of a ... | A. front. | multi_object_viewpoint_towards_object | 0017ccd0b1865cb8.jpg |
001820dafd878457_8c4a | Consider the real-world 3D location of the objects. Which object is closer to the camera? | a parking lot | a tree with green leaves | null | null | [
{
"bbox_3d": [
-2.9,
0.2,
10.7
],
"label": "a parking lot"
},
{
"bbox_3d": [
-3.7,
5.7,
25.9
],
"label": "a tree with green leaves"
}
] | [] | A | To solve this problem, we first estimate the 3D locations of a parking lot and a tree with green leaves. Then we estimate the L2 distances from the camera to the two objects. The object with a smaller L2 distance is the one that is closer to the camera. The 3D location of a parking lot is (-2.9, 0.2, 10.7). The 3D loca... | A. a parking lot. | location_closer_to_camera | 001820dafd878457.jpg |
001849a427c04bdf_474e | Consider the real-world 3D locations and orientations of the objects. Which side of a black speaker with a white label is facing a conference hall with a projector screen? | front | left | back | right | [
{
"bbox_3d": [
-3.1,
2.9,
8.1
],
"label": "a black speaker with a white label"
},
{
"bbox_3d": [
-0.4,
2.4,
8.2
],
"label": "a conference hall with a projector screen"
}
] | [
{
"front_dir": [
0.4,
-0.2,
-0.9
],
"label": "a black speaker with a white label",
"left_dir": [
-0.9,
0,
-0.4
]
}
] | D | To solve this problem, we first detect the 3D locations of a black speaker with a white label and a conference hall with a projector screen. Then we compute the vector pointing from a black speaker with a white label to a conference hall with a projector screen. Now we compute the angles between the vector and the left... | D. right. | multi_object_viewpoint_towards_object | 001849a427c04bdf.jpg |
0018645944496abb_ab91 | Consider the real-world 3D locations and orientations of the objects. Which side of a stone building with a castle-like appearance is facing the camera? | front | left | back | right | [
{
"bbox_3d": [
-3.4,
7.7,
22.1
],
"label": "a stone building with a castle-like appearance"
}
] | [
{
"front_dir": [
-0.1,
-0.4,
-0.9
],
"label": "a stone building with a castle-like appearance",
"left_dir": [
-1,
0.1,
0.1
]
}
] | A | To solve this problem, we first estimate the 3D location of a stone building with a castle-like appearance. Then we obtain the vector pointing from the object to the camera. Now we compute the angles between the vector and the left, right, front, back directions. We first compute the left direction of a stone building ... | A. front. | orientation_viewpoint | 0018645944496abb.jpg |
0018c1260fa26469_9066 | Consider the real-world 3D orientations of the objects. Are a black and white car and a car with a light on facing same or similar directions, or very different directions? | same or similar directions | very different directions | null | null | [
{
"bbox_3d": [
4.3,
19.3,
107.2
],
"label": "a black and white car"
},
{
"bbox_3d": [
3.2,
19.3,
106.9
],
"label": "a car with a light on"
}
] | [
{
"front_dir": [
0,
-0.3,
-1
],
"label": "a black and white car",
"left_dir": [
-1,
0,
0
]
},
{
"front_dir": [
0,
-0.3,
-1
],
"label": "a car with a light on",
"left_dir": [
-1,
0,
0
]
}
] | A | To solve this problem, we first detect the front directions of a black and white car and a car with a light on. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is small, then the two objects are facing same or similar di... | A. same or similar directions. | multi_object_same_direction | 0018c1260fa26469.jpg |
001905b029f4e1f1_be41 | Consider the real-world 3D location of the objects. Which object is closer to the camera? | a man in a green shirt holding up his hand | a man wearing a black and white jersey | null | null | [
{
"bbox_3d": [
-2.3,
2.8,
18.4
],
"label": "a man in a green shirt holding up his hand"
},
{
"bbox_3d": [
-1.2,
0.4,
8.8
],
"label": "a man wearing a black and white jersey"
}
] | [] | B | To solve this problem, we first estimate the 3D locations of a man in a green shirt holding up his hand and a man wearing a black and white jersey. Then we estimate the L2 distances from the camera to the two objects. The object with a smaller L2 distance is the one that is closer to the camera. The 3D location of a ma... | B. a man wearing a black and white jersey. | location_closer_to_camera | 001905b029f4e1f1.jpg |
001952f2e3bf13a5_0eb5 | Consider the real-world 3D orientations of the objects. Are a white keyboard with black keys and a white computer with a black screen facing same or similar directions, or very different directions? | same or similar directions | very different directions | null | null | [
{
"bbox_3d": [
-0.2,
0.5,
0.9
],
"label": "a white keyboard with black keys"
},
{
"bbox_3d": [
-0.3,
0.7,
0.8
],
"label": "a white computer with a black screen"
}
] | [
{
"front_dir": [
0.3,
-0.9,
-0.3
],
"label": "a white keyboard with black keys",
"left_dir": [
-0.9,
-0.1,
-0.5
]
},
{
"front_dir": [
0.4,
-0.9,
-0.2
],
"label": "a white computer with a black screen",
"left_dir": [
... | A | To solve this problem, we first detect the front directions of a white keyboard with black keys and a white computer with a black screen. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is small, then the two objects are... | A. same or similar directions. | multi_object_same_direction | 001952f2e3bf13a5.jpg |
0019b68c36d104fb_1121 | Consider the real-world 3D locations and orientations of the objects. If I stand at a piano with a brown wooden frame's position facing where it is facing, is a white car with a black hood on the left or right of me? | on the left | on the right | null | null | [
{
"bbox_3d": [
-1.3,
0.4,
3.9
],
"label": "a white car with a black hood"
},
{
"bbox_3d": [
-0.2,
0.3,
0.9
],
"label": "a piano with a brown wooden frame"
}
] | [
{
"front_dir": [
0.9,
-0.3,
0.2
],
"label": "a white car with a black hood",
"left_dir": [
0.1,
-0.2,
-1
]
},
{
"front_dir": [
-0.9,
0.3,
-0.2
],
"label": "a piano with a brown wooden frame",
"left_dir": [
-0.3,
... | A | To solve this problem, we first determine the 3D locations of a white car with a black hood and a piano with a brown wooden frame. Then we estimate the vector pointing from a piano with a brown wooden frame to a white car with a black hood, as well as the left direction of a piano with a brown wooden frame. Next we com... | A. on the left. | orientation_on_the_left | 0019b68c36d104fb.jpg |
001ae662d690368e_4003 | Consider the real-world 3D locations and orientations of the objects. Which side of a white and blue trailer is facing the camera? | front | left | back | right | [
{
"bbox_3d": [
-13.1,
-6,
95
],
"label": "a white and blue trailer"
}
] | [
{
"front_dir": [
0.2,
0.2,
-1
],
"label": "a white and blue trailer",
"left_dir": [
-1,
0.1,
-0.2
]
}
] | A | To solve this problem, we first estimate the 3D location of a white and blue trailer. Then we obtain the vector pointing from the object to the camera. Now we compute the angles between the vector and the left, right, front, back directions. We first compute the left direction of a white and blue trailer, which leads t... | A. front. | orientation_viewpoint | 001ae662d690368e.jpg |
001ccf6254ebf36f_a35c | Consider the real-world 3D locations of the objects. Which is closer to a tree with green leaves, a white bench in a park or a white bench in a park? | a white bench in a park | a white bench in a park | null | null | [
{
"bbox_3d": [
2,
2.6,
5.9
],
"label": "a tree with green leaves"
},
{
"bbox_3d": [
0.6,
0.7,
4.4
],
"label": "a white bench in a park"
},
{
"bbox_3d": [
-1.4,
3.1,
6.5
],
"label": "a white bench in a park"
}
] | [] | A | To solve this problem, we first detect the 3D location of a tree with green leaves, a white bench in a park, and a white bench in a park. Then we compute the L2 distances between a tree with green leaves and a white bench in a park, and between a tree with green leaves and a white bench in a park. The object that is cl... | A. a white bench in a park. | multi_object_closer_to | 001ccf6254ebf36f.jpg |
001d5341337eecf7_6dcf | Consider the real-world 3D orientations of the objects. Are a red car and a white car facing same or similar directions, or very different directions? | same or similar directions | very different directions | null | null | [
{
"bbox_3d": [
15.4,
0.5,
33.6
],
"label": "a red car"
},
{
"bbox_3d": [
8.1,
1,
20.5
],
"label": "a white car"
}
] | [
{
"front_dir": [
-0.3,
-0.1,
-0.9
],
"label": "a red car",
"left_dir": [
-0.9,
0,
0.3
]
},
{
"front_dir": [
-0.3,
-0.1,
-1
],
"label": "a white car",
"left_dir": [
-1,
0,
0.3
]
}
] | A | To solve this problem, we first detect the front directions of a red car and a white car. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is small, then the two objects are facing same or similar directions. Otherwise, t... | A. same or similar directions. | multi_object_same_direction | 001d5341337eecf7.jpg |
001d8d372f4b680d_a347 | Consider the real-world 3D locations of the objects. Is a rock with a red leaf on it directly underneath a large stone with a green netting? | yes | no | null | null | [
{
"bbox_3d": [
-1,
2,
3.8
],
"label": "a large stone with a green netting"
},
{
"bbox_3d": [
0.7,
0.6,
3.5
],
"label": "a rock with a red leaf on it"
}
] | [] | B | To solve this problem, we first determine the 3D locations of a large stone with a green netting and a rock with a red leaf on it. Then we compute the vector pointing from a rock with a red leaf on it to a large stone with a green netting, as well as the up direction of a rock with a red leaf on it. We estimate the cos... | B. no. | location_above | 001d8d372f4b680d.jpg |
001e040e9f8a2d4f_914e | Consider the real-world 3D locations and orientations of the objects. Which side of a boat made of wood is facing the camera? | front | left | back | right | [
{
"bbox_3d": [
-0.1,
2.4,
5.3
],
"label": "a boat made of wood"
}
] | [
{
"front_dir": [
0.1,
-0.1,
-1
],
"label": "a boat made of wood",
"left_dir": [
-1,
0.1,
-0.1
]
}
] | A | To solve this problem, we first estimate the 3D location of a boat made of wood. Then we obtain the vector pointing from the object to the camera. Now we compute the angles between the vector and the left, right, front, back directions. We first compute the left direction of a boat made of wood, which leads to the angl... | A. front. | orientation_viewpoint | 001e040e9f8a2d4f.jpg |
001e5022bb22230c_a470 | Consider the real-world 3D location of the objects. Which object is closer to the camera? | a man in a black shirt | a mall with many people walking around | null | null | [
{
"bbox_3d": [
-1.2,
0.5,
3.4
],
"label": "a man in a black shirt"
},
{
"bbox_3d": [
-0.3,
3.1,
7.5
],
"label": "a mall with many people walking around"
}
] | [] | A | To solve this problem, we first estimate the 3D locations of a man in a black shirt and a mall with many people walking around. Then we estimate the L2 distances from the camera to the two objects. The object with a smaller L2 distance is the one that is closer to the camera. The 3D location of a man in a black shirt i... | A. a man in a black shirt. | location_closer_to_camera | 001e5022bb22230c.jpg |
001ea8a8ca78a3bc_adce | Consider the real-world 3D locations of the objects. Which is closer to a man in a suit, a woman in a blue shirt or a man in blue shirt talking? | a woman in a blue shirt | a man in blue shirt talking | null | null | [
{
"bbox_3d": [
-0.2,
0.5,
1
],
"label": "a man in a suit"
},
{
"bbox_3d": [
-0.2,
0.5,
2
],
"label": "a woman in a blue shirt"
},
{
"bbox_3d": [
-0.3,
1.3,
3.2
],
"label": "a man in blue shirt talking"
}
] | [] | A | To solve this problem, we first detect the 3D location of a man in a suit, a woman in a blue shirt, and a man in blue shirt talking. Then we compute the L2 distances between a man in a suit and a woman in a blue shirt, and between a man in a suit and a man in blue shirt talking. The object that is closer to a man in a ... | A. a woman in a blue shirt. | multi_object_closer_to | 001ea8a8ca78a3bc.jpg |
001edd1f82b68837_a648 | Consider the real-world 3D locations of the objects. Is a black paddle directly above a boat with a blue and orange stripe? | yes | no | null | null | [
{
"bbox_3d": [
-1.8,
1,
9.6
],
"label": "a black paddle"
},
{
"bbox_3d": [
10.1,
-2.4,
59.8
],
"label": "a boat with a blue and orange stripe"
}
] | [] | B | To solve this problem, we first determine the 3D locations of a black paddle and a boat with a blue and orange stripe. Then we compute the vector pointing from a boat with a blue and orange stripe to a black paddle, as well as the up direction of a boat with a blue and orange stripe. We estimate the cosine similarity b... | B. no. | location_above | 001edd1f82b68837.jpg |
001ee3ac76b45faf_af06 | Consider the real-world 3D locations of the objects. Are the a tall tower with a pointed roof and the a clock with a stone wall next to each other or far away from each other? | next to each other | far away from each other | null | null | [
{
"bbox_3d": [
-9.8,
49.1,
90.7
],
"label": "a tall tower with a pointed roof"
},
{
"bbox_3d": [
18.6,
29.9,
69.8
],
"label": "a clock with a stone wall"
}
] | [] | A | To solve this problem, we first estimate the 3D locations of a tall tower with a pointed roof and a clock with a stone wall. Then we can compute the L2 distance between the two objects. Next we estimate the rough sizes of the two objects. If the distance between the two objects is smaller or roughly the same as the obj... | A. next to each other. | location_next_to | 001ee3ac76b45faf.jpg |
001f480aa6899a21_62a2 | Consider the real-world 3D locations and orientations of the objects. If I stand at a black seat's position facing where it is facing, is a man wearing a black hat on the left or right of me? | on the left | on the right | null | null | [
{
"bbox_3d": [
0.2,
0.6,
1
],
"label": "a man wearing a black hat"
},
{
"bbox_3d": [
0.6,
0.5,
1.2
],
"label": "a black seat"
}
] | [
{
"front_dir": [
-0.4,
-0.2,
-0.9
],
"label": "a black seat",
"left_dir": [
-0.9,
0.1,
0.3
]
}
] | A | To solve this problem, we first determine the 3D locations of a man wearing a black hat and a black seat. Then we estimate the vector pointing from a black seat to a man wearing a black hat, as well as the left direction of a black seat. Next we compute the cosine similarities between the vector and the left direction,... | A. on the left. | orientation_on_the_left | 001f480aa6899a21.jpg |
001f4cbc9bc272e7_3884 | Consider the real-world 3D locations of the objects. Are the a bald man in a blue shirt and the a woman in a gray shirt standing next to each other or far away from each other? | next to each other | far away from each other | null | null | [
{
"bbox_3d": [
0.3,
1,
0.9
],
"label": "a bald man in a blue shirt"
},
{
"bbox_3d": [
-0.3,
0.9,
2.5
],
"label": "a woman in a gray shirt standing"
}
] | [] | A | To solve this problem, we first estimate the 3D locations of a bald man in a blue shirt and a woman in a gray shirt standing. Then we can compute the L2 distance between the two objects. Next we estimate the rough sizes of the two objects. If the distance between the two objects is smaller or roughly the same as the ob... | A. next to each other. | location_next_to | 001f4cbc9bc272e7.jpg |
001f8d02b88a190d_c8fc | Consider the real-world 3D locations and orientations of the objects. If I stand at a black leather seat's position facing where it is facing, is a man wearing a hat in front of me or behind me? | in front of | behind | null | null | [
{
"bbox_3d": [
-0.5,
0.9,
1.1
],
"label": "a man wearing a hat"
},
{
"bbox_3d": [
0,
0.7,
1
],
"label": "a black leather seat"
}
] | [
{
"front_dir": [
-1,
0.1,
-0.1
],
"label": "a black leather seat",
"left_dir": [
-0.1,
0.1,
1
]
}
] | A | To solve this problem, we first determine the 3D locations of a man wearing a hat and a black leather seat. Then we estimate the vector pointing from a black leather seat to a man wearing a hat, as well as the front direction of a black leather seat. Next we compute the cosine similarities between the vector and the fr... | A. in front of. | orientation_in_front_of | 001f8d02b88a190d.jpg |
001fb08c97bbdf6b_e9fc | Consider the real-world 3D locations of the objects. Which object has a higher location? | a man wearing a tie | a man signing a book | null | null | [
{
"bbox_3d": [
0.7,
1.1,
3.6
],
"label": "a man wearing a tie"
},
{
"bbox_3d": [
-0.3,
1,
1.4
],
"label": "a man signing a book"
}
] | [] | A | To solve this problem, we first estimate the 3D heights of the two objects. The object with a larger height value is at a higher location. The object with a smaller height value is at a lower location. The 3D height of a man wearing a tie is 2.9. The 3D height of a man signing a book is 2.0. The 3D height of a man wear... | A. a man signing a book. | height_higher | 001fb08c97bbdf6b.jpg |
00200ddc8b80344f_f56b | Consider the real-world 3D locations of the objects. Is a yellow bead with a blue design directly underneath a white bracelet? | yes | no | null | null | [
{
"bbox_3d": [
-0.3,
0.6,
1.2
],
"label": "a white bracelet"
},
{
"bbox_3d": [
0,
0.3,
0.4
],
"label": "a yellow bead with a blue design"
}
] | [] | B | To solve this problem, we first determine the 3D locations of a white bracelet and a yellow bead with a blue design. Then we compute the vector pointing from a yellow bead with a blue design to a white bracelet, as well as the up direction of a yellow bead with a blue design. We estimate the cosine similarity between t... | B. no. | location_above | 00200ddc8b80344f.jpg |
002114082087da38_53a6 | Consider the real-world 3D locations of the objects. Which is closer to a large stone statue, a man in brown sweater or a man in a blue shirt? | a man in brown sweater | a man in a blue shirt | null | null | [
{
"bbox_3d": [
-1,
1,
6.2
],
"label": "a large stone statue"
},
{
"bbox_3d": [
-1.8,
1,
18.8
],
"label": "a man in brown sweater"
},
{
"bbox_3d": [
-1.7,
0.7,
25.8
],
"label": "a man in a blue shirt"
}
] | [] | A | To solve this problem, we first detect the 3D location of a large stone statue, a man in brown sweater, and a man in a blue shirt. Then we compute the L2 distances between a large stone statue and a man in brown sweater, and between a large stone statue and a man in a blue shirt. The object that is closer to a large st... | A. a man in brown sweater. | multi_object_closer_to | 002114082087da38.jpg |
002150b620f2aa6d_96f2 | Consider the real-world 3D locations of the objects. Are the a blue car and the a statue of a man pointing upwards next to each other or far away from each other? | next to each other | far away from each other | null | null | [
{
"bbox_3d": [
-2.7,
0.2,
12
],
"label": "a blue car"
},
{
"bbox_3d": [
0.8,
4.1,
11.7
],
"label": "a statue of a man pointing upwards"
}
] | [] | A | To solve this problem, we first estimate the 3D locations of a blue car and a statue of a man pointing upwards. Then we can compute the L2 distance between the two objects. Next we estimate the rough sizes of the two objects. If the distance between the two objects is smaller or roughly the same as the object sizes, th... | A. next to each other. | location_next_to | 002150b620f2aa6d.jpg |
00222ebc32fc7060_94eb | Consider the real-world 3D orientations of the objects. Are a metal stool and a wooden chair with a white table facing same or similar directions, or very different directions? | same or similar directions | very different directions | null | null | [
{
"bbox_3d": [
-2.1,
1.2,
6.9
],
"label": "a metal stool"
},
{
"bbox_3d": [
-2.9,
1.4,
6.9
],
"label": "a wooden chair with a white table"
}
] | [
{
"front_dir": [
-0.1,
-0.1,
-1
],
"label": "a metal stool",
"left_dir": [
-1,
0.2,
0.1
]
},
{
"front_dir": [
1,
-0.3,
0
],
"label": "a wooden chair with a white table",
"left_dir": [
-0.1,
-0.4,
-0.9
... | B | To solve this problem, we first detect the front directions of a metal stool and a wooden chair with a white table. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is small, then the two objects are facing same or simila... | B. very different directions. | multi_object_same_direction | 00222ebc32fc7060.jpg |
0022fa6e735219dc_1628 | Consider the real-world 3D locations of the objects. Which object has a lower location? | a woman wearing a black skirt | a building with a shingled roof | null | null | [
{
"bbox_3d": [
0.8,
0.3,
4
],
"label": "a woman wearing a black skirt"
},
{
"bbox_3d": [
1.1,
0.7,
3.5
],
"label": "a building with a shingled roof"
}
] | [] | A | To solve this problem, we first estimate the 3D heights of the two objects. The object with a larger height value is at a higher location. The object with a smaller height value is at a lower location. The 3D height of a woman wearing a black skirt is 0.7. The 3D height of a building with a shingled roof is 1.3. The 3D... | A. a woman wearing a black skirt | height_higher | 0022fa6e735219dc.jpg |
00230cac98ad3f54_aebd | Consider the real-world 3D locations and orientations of the objects. Which object is a green motorcycle facing towards, a brick wall or the a bicycle with a round wheel? | a brick wall | a bicycle with a round wheel | null | null | [
{
"bbox_3d": [
1.1,
1,
7.5
],
"label": "a green motorcycle"
},
{
"bbox_3d": [
-1,
1.5,
3.6
],
"label": "a brick wall"
},
{
"bbox_3d": [
0.7,
0.9,
11
],
"label": "a bicycle with a round wheel"
}
] | [
{
"front_dir": [
-0.6,
-0.1,
-0.8
],
"label": "a green motorcycle",
"left_dir": [
-0.8,
0.1,
0.6
]
},
{
"front_dir": [
0,
0,
-1
],
"label": "a bicycle with a round wheel",
"left_dir": [
-1,
0,
0
]
}... | A | To solve this problem, we first detect the 3D location of a green motorcycle, a brick wall, and a bicycle with a round wheel. Then we compute the cosine similarities between the front direction of a green motorcycle and the vectors from a green motorcycle to the other two objects. We can estimate the angles from the co... | A. a brick wall. | multi_object_facing | 00230cac98ad3f54.jpg |
00240903981cd456_456b | Consider the real-world 3D locations of the objects. Are the a pink flower with purple petals and the a red roof with a white house next to each other or far away from each other? | next to each other | far away from each other | null | null | [
{
"bbox_3d": [
0.1,
0.6,
2.1
],
"label": "a pink flower with purple petals"
},
{
"bbox_3d": [
-0.2,
4.2,
9.6
],
"label": "a red roof with a white house"
}
] | [] | A | To solve this problem, we first estimate the 3D locations of a pink flower with purple petals and a red roof with a white house. Then we can compute the L2 distance between the two objects. Next we estimate the rough sizes of the two objects. If the distance between the two objects is smaller or roughly the same as the... | A. next to each other. | location_next_to | 00240903981cd456.jpg |
002435b9db3c4a03_9814 | Consider the real-world 3D orientations of the objects. Are a wooden podium with a microphone and a green bulletin board facing same or similar directions, or very different directions? | same or similar directions | very different directions | null | null | [
{
"bbox_3d": [
1.4,
1.1,
4.6
],
"label": "a wooden podium with a microphone"
},
{
"bbox_3d": [
3.1,
1.8,
5.5
],
"label": "a green bulletin board"
}
] | [
{
"front_dir": [
-0.7,
0.1,
-0.7
],
"label": "a wooden podium with a microphone",
"left_dir": [
-0.7,
-0.2,
0.7
]
},
{
"front_dir": [
-1,
0.3,
-0.2
],
"label": "a green bulletin board",
"left_dir": [
-0.2,
-0.2... | A | To solve this problem, we first detect the front directions of a wooden podium with a microphone and a green bulletin board. Then we compute the cosine similarities between the two front directions, and the angle between them. If the angle between the two front directions is small, then the two objects are facing same ... | A. same or similar directions. | multi_object_same_direction | 002435b9db3c4a03.jpg |
0024863edfb352bc_a57c | Consider the real-world 3D locations and orientations of the objects. Which object is a blue car facing towards, a parking lot with cars or the a blue car? | a parking lot with cars | a blue car | null | null | [
{
"bbox_3d": [
-15,
-0.6,
31.6
],
"label": "a blue car"
},
{
"bbox_3d": [
-0.3,
0.7,
9.2
],
"label": "a parking lot with cars"
},
{
"bbox_3d": [
-12.3,
-0.3,
32.6
],
"label": "a blue car"
}
] | [
{
"front_dir": [
0.5,
0,
-0.9
],
"label": "a blue car",
"left_dir": [
-0.9,
0.1,
-0.5
]
},
{
"front_dir": [
0.4,
0,
-0.9
],
"label": "a blue car",
"left_dir": [
-0.9,
0.1,
-0.4
]
}
] | A | To solve this problem, we first detect the 3D location of a blue car, a parking lot with cars, and a blue car. Then we compute the cosine similarities between the front direction of a blue car and the vectors from a blue car to the other two objects. We can estimate the angles from the cosine similarities, and the obje... | A. a parking lot with cars. | multi_object_facing | 0024863edfb352bc.jpg |
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