Ruicheng/monocular-geometry-evaluation

Processed versions of some open-source datasets for evaluation of monocular geometry estimation.

Dataset	Source	Publication	Num images	Storage Size	Note
NYUv2	NYU Depth Dataset V2	[1]	654	243 MB	Offical test split. Mirror, glass and window manually removed. Depth beyound 5 m truncated.
KITTI	KITTI Vision Benchmark Suite	[2, 3]	652	246 MB	Eigen's test split.
ETH3D	ETH3D SLAM & Stereo Benchmarks	[4]	454	1.3 GB	Downsized from 6202×4135 to 2048×1365
iBims-1	iBims-1 (independent Benchmark images and matched scans - version 1)	[5, 6]	100	41 MB
GSO	Google Scanned Objects	[7]	1030	94 MB	Rendered images at 512x512 resolution.
Sintel	MPI Sintel Dataset	[8]	1064	530 MB	Sky regions manually removed.
DDAD	DDAD - Dense Depth for Autonomous Driving	[9]	1000	593 MB	Uniformly selected from the original validation set. The vechile itself occupies a small portion of the image, which is cropped out.
DIODE	DIODE: A Dense Indoor and Outdoor DEpth Dataset	[10]	771	558 MB	Validation split, 325 indoor and 458 outdoor images. Boundary artifacts filtered. FOV corrected as addressed in this issue
Spring	Spring dataset and benchmark	[11]	1000	1.6 GB	Uniformly selected 1/5 of the frames from the original 5000 frames.
HAMMER	HAMMER - Highly Accurate Multi-Modal Dataset for DEnse 3D Scene Regression	[12]	775	397 MB

Download

Make sure huggingface_hub is installed. If not installed:

pip install huggingface_hub

Download:

huggingface-cli download Ruicheng/monocular-geometry-evaluation --repo-type dataset --local-dir /PATH/TO/DOWNLOAD/DIRECTORY --local-dir-use-symlinks False

Extract the zip files: (each dataset has a separate zip file)

cd /PATH/TO/DOWNLOAD/DIRECTORY
unzip '*.zip'

Please refer to https://github.com/microsoft/MoGe/blob/main/moge/utils/io.py for reading the depth maps.

Reference

[1] Silberman, N., Hoiem, D., Kohli, P. and Fergus, R., 2012. Indoor segmentation and support inference from rgbd images. In Computer Vision–ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part V 12 (pp. 746-760). Springer Berlin Heidelberg.

[2] Geiger, A., Lenz, P. and Urtasun, R., 2012, June. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE conference on computer vision and pattern recognition (pp. 3354-3361). IEEE.

[3] Geiger, A., Lenz, P., Stiller, C. and Urtasun, R., 2013. Vision meets robotics: The kitti dataset. The international journal of robotics research, 32(11), pp.1231-1237.

[4] T. Schöps, T. Sattler, M. Pollefeys, "BAD SLAM: Bundle Adjusted Direct RGB-D SLAM", Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

[5] Koch, Tobias; Liebel, Lukas; Körner, Marco; Fraundorfer, Friedrich: Comparison of monocular depth estimation methods using geometrically relevant metrics on the IBims-1 dataset. Computer Vision and Image Understanding (CVIU) 191, 2020, 102877

[6] Koch, Tobias; Liebel, Lukas; Fraundorfer, Friedrich; Körner, Marco: Evaluation of CNN-Based Single-Image Depth Estimation Methods. Proceedings of the European Conference on Computer Vision Workshops (ECCV-WS), Springer International Publishing, 2019, 331-348

[7] Downs, L., Francis, A., Koenig, N., Kinman, B., Hickman, R., Reymann, K., McHugh, T.B. and Vanhoucke, V., 2022, May. Google scanned objects: A high-quality dataset of 3d scanned household items. In 2022 International Conference on Robotics and Automation (ICRA) (pp. 2553-2560). IEEE.

[8] Butler, D.J., Wulff, J., Stanley, G.B. and Black, M.J., 2012. A naturalistic open source movie for optical flow evaluation. In Computer Vision–ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part VI 12 (pp. 611-625). Springer Berlin Heidelberg.

[9] Guizilini, V., Ambrus, R., Pillai, S., Raventos, A. and Gaidon, A., 2020. 3d packing for self-supervised monocular depth estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2485-2494).

[10] Vasiljevic, I., Kolkin, N., Zhang, S., Luo, R., Wang, H., Dai, F.Z., Daniele, A.F., Mostajabi, M., Basart, S., Walter, M.R. and Shakhnarovich, G., 2019. Diode: A dense indoor and outdoor depth dataset. arXiv preprint arXiv:1908.00463.

[11] Mehl, L., Schmalfuss, J., Jahedi, A., Nalivayko, Y. and Bruhn, A., 2023. Spring: A high-resolution high-detail dataset and benchmark for scene flow, optical flow and stereo. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4981-4991).

[12] Jung, H., Ruhkamp, P., Zhai, G., Brasch, N., Li, Y., Verdie, Y., Song, J., Zhou, Y., Armagan, A., Ilic, S. and Leonardis, A., 2023. On the importance of accurate geometry data for dense 3D vision tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 780-791).