Datasets › WOD-C

WOD-C

Introduced by Lingdong Kong et al. in Robo3D: Towards Robust and Reliable 3D Perception against Corruptions30 Mar 2023 archive 2025-07-28

🤖 Robo3D - The WOD-C Benchmark

WOD-C is an evaluation benchmark heading toward robust and reliable 3D perception in autonomous driving. With it, we probe the robustness of 3D detectors and segmentors under out-of-distribution (OoD) scenarios against corruptions that occur in the real-world environment. Specifically, we consider natural corruptions happen in the following cases:

  • Adverse weather conditions, such as fog, wet ground, and snow;
  • External disturbances that are caused by motion blur or result in LiDAR beam missing;
  • Internal sensor failure, including crosstalk, possible incomplete echo, and cross-sensor scenarios.

WOD-C is part of the Robo3D benchmark. Visit our homepage to explore more details.

Benchmarks archive 2025-07-28

All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Robust 3D Semantic Segmentation WOD-C MinkUNet-34 mean Corruption Error (mCE) 96.21% 4D Spatio-Temporal ConvNets: Minkowski Convolutional... NVIDIA/MinkowskiEngine +7 5 Compare

Papers archive 2025-07-28

3 shown of 3 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 5. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation 2 1 19 Nov 2020 not harvested
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution 6 2 31 Jul 2020 ran 0 of 3 samples (3 unverified)
4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks 8 2 18 Apr 2019 ran 1 of 2 samples (1 unverified; 2 pointer-only for licence)

Dataset loaders archive 2025-07-28

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License

Modalities archive 2025-07-28

Languages archive 2025-07-28

Variants archive 2025-07-28

  • WOD-C

1 variant name, as the archive lists them.

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