</> Code with Papers by Syntology

Datasets › nuScenes-C

nuScenes-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 nuScenes-C Benchmark

nuScenes-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.

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

HomepageBenchmarksPapers JSON

Benchmarks archive 2025-07-28

All 2 leaderboards 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 nuScenes-C GFNet mean Corruption Error (mCE) 92.55% GFNet: Geometric Flow Network for 3D Point Cloud... haibo-qiu/gfnet 12 Compare
Robust 3D Object Detection nuScenes-C CenterPoint-PP mean Corruption Error (mCE) 100.00 Center-based 3D Object Detection and Tracking open-mmlab/mmdetection3d +12 1 Compare

Papers archive 2025-07-28

10 shown of 10 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 33. 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
Using a Waffle Iron for Automotive Point Cloud Semantic Segmentation 1 1 24 Jan 2023 ran 5 of 5 samples (0 unverified; 5 pointer-only for licence)
CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving 3 1 26 Jul 2022 ran 0 of 1 samples (1 unverified)
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds 1 1 10 Jul 2022 not harvested
GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentation 1 1 6 Jul 2022 not harvested
FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding 1 1 8 Sep 2021 not harvested
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation 2 2 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)
Center-based 3D Object Detection and Tracking 13 1 19 Jun 2020 ran 7 of 22 samples (15 unverified)
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation 4 1 31 Mar 2020 ran 9 of 11 samples (2 unverified; 1 pointer-only for licence)
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

Daniel-xsy/RoboBEVpytorch
ldkong1205/Robo3Dpytorch

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

Tasks archive 2025-07-28

3D Object Detection3D Semantic SegmentationRobust 3D Semantic SegmentationRobust 3D Object DetectionRobust Camera Only 3D Object Detection

License archive 2025-07-28

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License

Modalities archive 2025-07-28

ImagesPoint cloud

Languages archive 2025-07-28

English

Variants archive 2025-07-28

  • nuScenes-C

1 variant name, as the archive lists them.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections