Dataset Viewer
id
int64 0
7.21k
| img_type
stringclasses 2
values | format_type
stringclasses 2
values | task
stringclasses 20
values | source
stringclasses 2
values | image
images listlengths 1
3
| depth
sequencelengths 1
3
| pose
sequencelengths 1
3
| intrinsic_color
sequencelengths 1
3
| intrinsic_depth
sequencelengths 1
3
| question
stringlengths 181
1.38k
| answer
stringclasses 475
values |
---|---|---|---|---|---|---|---|---|---|---|---|
0 |
single_view
|
fill
|
depth_prediction_oc
|
scannet
| [{"src":"https://datasets-server.huggingface.co/assets/jasonzhango/SPAR-Bench-RGBD/--/{dataset_git_r(...TRUNCATED) | [[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0(...TRUNCATED) | [[-0.44636398553848267,-0.4912540018558502,0.7479490041732788,3.190169095993042,-0.8934580087661743,(...TRUNCATED) |
[
[
1170.18798828125,
0,
647.75,
0,
0,
1170.18798828125,
483.75,
0,
0,
0,
1,
0,
0,
0,
0,
1
]
] |
[
[
577.87060546875,
0,
319.5,
0,
0,
577.87060546875,
239.5,
0,
0,
0,
1,
0,
0,
0,
0,
1
]
] | "The camera coordinates show the towel (red point) at 1.0 meters depth. What is the depth of the tow(...TRUNCATED) |
0.9
|
1 |
single_view
|
fill
|
depth_prediction_oc
|
scannet
| [{"src":"https://datasets-server.huggingface.co/assets/jasonzhango/SPAR-Bench-RGBD/--/{dataset_git_r(...TRUNCATED) | [[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0(...TRUNCATED) | [[-0.5540639758110046,-0.2759700119495392,0.7854009866714478,2.062014102935791,-0.8290389776229858,0(...TRUNCATED) | [[1178.3519287109375,0.0,647.75,0.0,0.0,1178.3519287109375,483.75,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.(...TRUNCATED) |
[
[
581.9021606445312,
0,
319.5,
0,
0,
581.9022216796875,
239.5,
0,
0,
0,
1,
0,
0,
0,
0,
1
]
] | "If the center of the chair (red point) is 3.7 meters deep, what is the depth of chair (blue point)?(...TRUNCATED) |
3.5
|
2 |
single_view
|
fill
|
depth_prediction_oc
|
scannet
| [{"src":"https://datasets-server.huggingface.co/assets/jasonzhango/SPAR-Bench-RGBD/--/{dataset_git_r(...TRUNCATED) | [[[2130.0,2130.0,2130.0,2130.0,2130.0,2130.0,2130.0,2130.0,2130.0,2130.0,2130.0,2115.0,2115.0,2115.0(...TRUNCATED) | [[0.9693179726600647,-0.20720000565052032,0.13224799931049347,1.2438119649887085,-0.2278919965028762(...TRUNCATED) |
[
[
577.87060546875,
0,
319.5,
0,
0,
577.87060546875,
239.5,
0,
0,
0,
1,
0,
0,
0,
0,
1
]
] |
[
[
577.87060546875,
0,
319.5,
0,
0,
577.87060546875,
239.5,
0,
0,
0,
1,
0,
0,
0,
0,
1
]
] | "Knowing that the center of chair (red point) is 1.8 meters deep, estimate the depth of bed (blue po(...TRUNCATED) |
1.6
|
3 |
single_view
|
fill
|
depth_prediction_oc
|
scannet
| [{"src":"https://datasets-server.huggingface.co/assets/jasonzhango/SPAR-Bench-RGBD/--/{dataset_git_r(...TRUNCATED) | [[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1429.0,1429.0,1429.0,1429.0,1429.0,1429.0,1429.0,1429(...TRUNCATED) | [[-0.8890349864959717,0.21207000315189362,-0.4057610034942627,2.9269580841064453,0.45516499876976013(...TRUNCATED) |
[
[
1170.18798828125,
0,
647.75,
0,
0,
1170.18798828125,
483.75,
0,
0,
0,
1,
0,
0,
0,
0,
1
]
] |
[
[
578,
0,
319.5,
0,
0,
578,
239.5,
0,
0,
0,
1,
0,
0,
0,
0,
1
]
] | "Given that toilet paper (red point) is at 1.6 meters, predict the depth of toilet (blue point). Ca(...TRUNCATED) |
1.5
|
4 |
single_view
|
fill
|
depth_prediction_oc
|
scannet
| [{"src":"https://datasets-server.huggingface.co/assets/jasonzhango/SPAR-Bench-RGBD/--/{dataset_git_r(...TRUNCATED) | [[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0(...TRUNCATED) | [[0.2932389974594116,0.33917999267578125,-0.8938500285148621,5.539703845977783,0.9327509999275208,-0(...TRUNCATED) | [[1169.62109375,0.0,646.2950439453125,0.0,0.0,1167.1051025390625,489.9270324707031,0.0,0.0,0.0,1.0,0(...TRUNCATED) | [[577.5906982421875,0.0,318.9054260253906,0.0,0.0,578.7297973632812,242.68360900878906,0.0,0.0,0.0,1(...TRUNCATED) | "The dresser (red point) is placed at a depth of 3.0 meters along the Z-axis(camera coordinate syste(...TRUNCATED) |
2.1
|
5 |
single_view
|
fill
|
depth_prediction_oc
|
scannet
| [{"src":"https://datasets-server.huggingface.co/assets/jasonzhango/SPAR-Bench-RGBD/--/{dataset_git_r(...TRUNCATED) | [[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0(...TRUNCATED) | [[0.9100059866905212,-0.15350499749183655,0.38512998819351196,4.489101886749268,-0.41239699721336365(...TRUNCATED) |
[
[
1170.18798828125,
0,
647.75,
0,
0,
1170.18798828125,
483.75,
0,
0,
0,
1,
0,
0,
0,
0,
1
]
] |
[
[
577.87060546875,
0,
319.5,
0,
0,
577.87060546875,
239.5,
0,
0,
0,
1,
0,
0,
0,
0,
1
]
] | "Using the chair (red point) depth of 3.4 meters, determine the depth of tv (blue point). Calculate(...TRUNCATED) |
3.0
|
6 |
single_view
|
fill
|
depth_prediction_oc
|
scannet
| [{"src":"https://datasets-server.huggingface.co/assets/jasonzhango/SPAR-Bench-RGBD/--/{dataset_git_r(...TRUNCATED) | [[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1177.0,1177.0,1181.0,1177.0,1177.(...TRUNCATED) | [[-0.9735140204429626,-0.13659200072288513,0.18333899974822998,4.473992824554443,-0.2260980010032653(...TRUNCATED) |
[
[
577.87060546875,
0,
319.5,
0,
0,
577.87060546875,
239.5,
0,
0,
0,
1,
0,
0,
0,
0,
1
]
] |
[
[
578,
0,
319.5,
0,
0,
578,
239.5,
0,
0,
0,
1,
0,
0,
0,
0,
1
]
] | "The depth of the cabinet (red point) at its center is measured as 1.6 meters. Estimate the depth of(...TRUNCATED) |
1.4
|
7 |
single_view
|
fill
|
depth_prediction_oc
|
scannet
| [{"src":"https://datasets-server.huggingface.co/assets/jasonzhango/SPAR-Bench-RGBD/--/{dataset_git_r(...TRUNCATED) | [[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0(...TRUNCATED) | [[0.6109970211982727,-0.003014999907463789,-0.7916269898414612,2.390932083129883,0.7916330099105835,(...TRUNCATED) |
[
[
1170.18798828125,
0,
647.75,
0,
0,
1170.18798828125,
483.75,
0,
0,
0,
1,
0,
0,
0,
0,
1
]
] |
[
[
577.87060546875,
0,
319.5,
0,
0,
577.87060546875,
239.5,
0,
0,
0,
1,
0,
0,
0,
0,
1
]
] | "The range hood (red point) is placed at a depth of 1.8 meters along the Z-axis(camera coordinate sy(...TRUNCATED) |
1.9
|
8 |
single_view
|
fill
|
depth_prediction_oc
|
scannet
| [{"src":"https://datasets-server.huggingface.co/assets/jasonzhango/SPAR-Bench-RGBD/--/{dataset_git_r(...TRUNCATED) | [[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0(...TRUNCATED) | [[-0.8749110102653503,-0.2052139937877655,0.43865498900413513,2.520699977874756,-0.48417699337005615(...TRUNCATED) | [[1163.445068359375,0.0,653.6260375976562,0.0,0.0,1164.7939453125,481.60003662109375,0.0,0.0,0.0,1.0(...TRUNCATED) | [[574.540771484375,0.0,322.5228271484375,0.0,0.0,577.583740234375,238.55885314941406,0.0,0.0,0.0,1.0(...TRUNCATED) | "Using sink (red point) at 1.6 meters as a guide, determine the depth of trash can (blue point). Ca(...TRUNCATED) |
1.9
|
9 |
single_view
|
fill
|
depth_prediction_oc
|
scannet
| [{"src":"https://datasets-server.huggingface.co/assets/jasonzhango/SPAR-Bench-RGBD/--/{dataset_git_r(...TRUNCATED) | [[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0(...TRUNCATED) | [[-0.9995409846305847,-0.0005740000051446259,-0.030302999541163445,1.2891939878463745,0.025614000856(...TRUNCATED) |
[
[
1170.18798828125,
0,
647.75,
0,
0,
1170.18798828125,
483.75,
0,
0,
0,
1,
0,
0,
0,
0,
1
]
] |
[
[
577.87060546875,
0,
319.5,
0,
0,
577.87060546875,
239.5,
0,
0,
0,
1,
0,
0,
0,
0,
1
]
] | "With trash can (red point) at 1.6 meters, calculate the depth of toilet (blue point). Calculate or(...TRUNCATED) |
1.3
|
End of preview. Expand
in Data Studio
YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/datasets-cards)
🎯 SPAR-Bench-RGBD
A depth-enhanced version of SPAR-Bench for evaluating 3D-aware spatial reasoning in vision-language models.
SPAR-Bench-RGBD extends the full SPAR-Bench with additional depths, camera intrinsics, and pose information, enabling evaluation of models with geometric or 3D-awareness capabilities.
The benchmark contains 7,207 manually verified QA pairs across 20 spatial tasks and supports single-view and multi-view inputs.
📥 Load with datasets
from datasets import load_dataset
spar_rgbd = load_dataset("jasonzhango/SPAR-Bench-RGBD")
🕹️ Evaluation
SPAR-Bench-RGBD uses the same evaluation protocol and metrics as the full SPAR-Bench.
We provide an evaluation pipeline in our GitHub repository, built on top of lmms-eval.
📚 Bibtex
If you find this project or dataset helpful, please consider citing our paper:
@article{zhang2025from,
title={From Flatland to Space: Teaching Vision-Language Models to Perceive and Reason in 3D},
author={Zhang, Jiahui and Chen, Yurui and Zhou, Yanpeng and Xu, Yueming and Huang, Ze and Mei, Jilin and Chen, Junhui and Yuan, Yujie and Cai, Xinyue and Huang, Guowei and Quan, Xingyue and Xu, Hang and Zhang, Li},
year={2025},
journal={arXiv preprint arXiv:2503.22976},
}
- Downloads last month
- 11