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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… See the full description on the dataset page: https://huggingface.co/datasets/Ruicheng/monocular-geometry-evaluation.

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

DatasetSourcePublicationNum imagesStorage SizeNote
NYUv2NYU Depth Dataset V2[1]654243 MBOffical test split. Mirror, glass and window manually removed. Depth beyound 5 m truncated.
KITTIKITTI Vision Benchmark Suite[2, 3]652246 MBEigen's test split.
ETH3DETH3D SLAM & Stereo Benchmarks[4]4541.3 GBDownsized from 6202×4135 to 2048×1365
iBims-1iBims-1 (independent Benchmark images and matched scans - version 1)[5, 6]10041 MB
GSOGoogle Scanned Objects[7]103094 MBRendered images at 512x512 resolution.
SintelMPI Sintel Dataset[8]1064530 MBSky regions manually removed.
DDADDDAD - Dense Depth for Autonomous Driving[9]1000593 MBUniformly selected from the original validation set. The vechile itself occupies a small portion of the image, which is cropped out.
DIODEDIODE: A Dense Indoor and Outdoor DEpth Dataset[10]771558 MBValidation split, 325 indoor and 458 outdoor images. Boundary artifacts filtered. FOV corrected as addressed in this issue
SpringSpring dataset and benchmark[11]10001.6 GBUniformly selected 1/5 of the frames from the original 5000 frames.
HAMMERHAMMER - Highly Accurate Multi-Modal Dataset for DEnse 3D Scene Regression[12]775397 MB

Download

Make sure huggingface_hub is installed. If not installed:

bash
pip install huggingface_hub

Download:

bash
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)

bash
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).