Papers › Meta Architecture for Point Cloud Analysis

Meta Architecture for Point Cloud Analysis

26 Nov 2022CVPR 2023 1arXiv:2211.14462archive 2025-07-28

Haojia Lin, Xiawu Zheng, Lijiang Li, Fei Chao, Shanshan Wang, Yan Wang, Yonghong Tian, Rongrong Ji

Recent advances in 3D point cloud analysis bring a diverse set of network architectures to the field. However, the lack of a unified framework to interpret those networks makes any systematic comparison, contrast, or analysis challenging, and practically limits healthy development of the field. In this paper, we take the initiative to explore and propose a unified framework called PointMeta, to which the popular 3D point cloud analysis approaches could fit. This brings three benefits. First, it allows us to compare different approaches in a fair manner, and use quick experiments to verify any empirical observations or assumptions summarized from the comparison. Second, the big picture brought by PointMeta enables us to think across different components, and revisit common beliefs and key design decisions made by the popular approaches. Third, based on the learnings from the previous two analyses, by doing simple tweaks on the existing approaches, we are able to derive a basic building block, termed PointMetaBase. It shows very strong performance in efficiency and effectiveness through extensive experiments on challenging benchmarks, and thus verifies the necessity and benefits of high-level interpretation, contrast, and comparison like PointMeta. In particular, PointMetaBase surpasses the previous state-of-the-art method by 0.7%/1.4/%2.1% mIoU with only 2%/11%/13% of the computation cost on the S3DIS datasets.

PaperPDFConference PDFCode

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

Code

linhaojia13/pointmetabase officialmentioned in papermentioned on GitHubpytorchMIT 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

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

3D Semantic SegmentationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Semantic Segmentation OpenTrench3D PointMetaBase-XXL Model Size 19.7M #2 of 3 Archive leaderboard report
3D Semantic Segmentation OpenTrench3D PointMetaBase-XXL mAcc 84.5 #2 of 3 Archive leaderboard report
3D Semantic Segmentation OpenTrench3D PointMetaBase-XXL mIoU 75.8 #2 of 3 Archive leaderboard report
Semantic Segmentation S3DIS PointMetaBase-XXL FLOPs 11.0G #9 of 54 Archive leaderboard report
Semantic Segmentation S3DIS PointMetaBase-XXL Mean IoU 77.0 #9 of 54 Archive leaderboard report
Semantic Segmentation S3DIS PointMetaBase-XXL Number of params 19.7M #9 of 54 Archive leaderboard report
Semantic Segmentation S3DIS PointMetaBase-XXL Params (M) 19.7 #9 of 54 Archive leaderboard report
Semantic Segmentation S3DIS PointMetaBase-XXL oAcc 91.3 #9 of 54 Archive leaderboard report
Semantic Segmentation S3DIS Area5 PointMetaBase-XXL Number of params N/A #28 of 61 Archive leaderboard report
Semantic Segmentation S3DIS Area5 PointMetaBase-XXL mIoU 71.3±0.7 #28 of 61 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