Papers › Benchmarking and Analyzing Point Cloud Classification under Corruptions

Benchmarking and Analyzing Point Cloud Classification under Corruptions

7 Feb 2022arXiv:2202.03377archive 2025-07-28

Jiawei Ren, Liang Pan, Ziwei Liu

3D perception, especially point cloud classification, has achieved substantial progress. However, in real-world deployment, point cloud corruptions are inevitable due to the scene complexity, sensor inaccuracy, and processing imprecision. In this work, we aim to rigorously benchmark and analyze point cloud classification under corruptions. To conduct a systematic investigation, we first provide a taxonomy of common 3D corruptions and identify the atomic corruptions. Then, we perform a comprehensive evaluation on a wide range of representative point cloud models to understand their robustness and generalizability. Our benchmark results show that although point cloud classification performance improves over time, the state-of-the-art methods are on the verge of being less robust. Based on the obtained observations, we propose several effective techniques to enhance point cloud classifier robustness. We hope our comprehensive benchmark, in-depth analysis, and proposed techniques could spark future research in robust 3D perception.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2202.03377")

Code

Syntology Ran 6 of 13 code samples harvested from 1 repository linked to this paper; 7 have no recorded run. Of those that ran: 4 ran · fixture could not drive it; 2 ran with no contract checked.

By repository: official repository: 13 samples from 1 repository, 6 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

jiawei-ren/modelnetc officialmentioned in papermentioned on GitHubpytorchApache-2.0 report
ldkong1205/PointCloud-C mentioned on GitHubpytorch report
yossilevii100/critical_points2 mentioned on GitHubpytorch report
yossilevii100/refocusing mentioned on GitHubpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

13 samples harvested; 6 ran; 0 honoured the contract we drafted; 7 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

4ran · fixture could not drive it
2ran
7unverified

Licence: 0 of the 13 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from jiawei-ren/modelnetc. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

get_graph_feature jiawei-ren/modelnetc/PointWOLF/model.py official repository ran · fixture could not drive it Apache-2.0 (permissive) · 9a8756778dd8234a · report
knn jiawei-ren/modelnetc/PointWOLF/model.py official repository ran · fixture could not drive it fingerprinted Apache-2.0 (permissive) · cdd0141594039dcb · report
load_h5 jiawei-ren/modelnetc/modelnetc_utils/modelnetc_utils/dataset.py official repository ran Apache-2.0 (permissive) · 1bfbbe6bae6f8e29 · report
rotate_point_cloud jiawei-ren/modelnetc/GDANet/provider.py official repository ran · fixture could not drive it fingerprinted Apache-2.0 (permissive) · adb85367d5bf360a · report
shuffle_data jiawei-ren/modelnetc/GDANet/provider.py official repository ran Apache-2.0 (permissive) · 06353aadebc1724b · report
shuffle_points jiawei-ren/modelnetc/GDANet/provider.py official repository ran · fixture could not drive it fingerprinted Apache-2.0 (permissive) · a97f76b9c0811c93 · report
corrupt_jitter jiawei-ren/modelnetc/build_modelnetc/corrupt_utils.py official repository unverified Apache-2.0 (permissive) · b5dd3f043c271b69 · report
corrupt_rotate jiawei-ren/modelnetc/build_modelnetc/corrupt_utils.py official repository unverified Apache-2.0 (permissive) · a260494677ed35d8 · report
corrupt_scale jiawei-ren/modelnetc/build_modelnetc/corrupt_utils.py official repository unverified Apache-2.0 (permissive) · 8f0a929253e9f007 · report
cut_points jiawei-ren/modelnetc/GDANet/rsmix_provider.py official repository unverified Apache-2.0 (permissive) · d26265da343f8191 · report
cut_points_knn jiawei-ren/modelnetc/GDANet/rsmix_provider.py official repository unverified Apache-2.0 (permissive) · f3bf3d79146b55df · report
knn_points jiawei-ren/modelnetc/GDANet/rsmix_provider.py official repository unverified Apache-2.0 (permissive) · a7032e8191925cc0 · report
load_data jiawei-ren/modelnetc/build_modelnetc/corrupt.py official repository unverified Apache-2.0 (permissive) · 6002e0aaef533772 · report

Tasks

BenchmarkingClassificationPoint Cloud Classification

Datasets

Introduced by this paper, per the archive.

PointCloud-C

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Point Cloud Classification PointCloud-C WOLFMix (GDANet) mean Corruption Error (mCE) 0.571 #8 of 24 Archive leaderboard report
Point Cloud Classification PointCloud-C WOLFMix (DGCNN) mean Corruption Error (mCE) 0.590 #9 of 24 Archive leaderboard report
Point Cloud Classification PointCloud-C WOLFMix (RPC) mean Corruption Error (mCE) 0.601 #10 of 24 Archive leaderboard report
Point Cloud Classification PointCloud-C RPC mean Corruption Error (mCE) 0.863 #13 of 24 Archive leaderboard report
Point Cloud Classification PointCloud-C WOLFMix (PointNet) mean Corruption Error (mCE) 1.180 #23 of 24 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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