Papers › Learning Inner-Group Relations on Point Clouds

Learning Inner-Group Relations on Point Clouds

27 Aug 2021ICCV 2021 10arXiv:2108.12468archive 2025-07-28

Haoxi Ran, Wei Zhuo, Jun Liu, Li Lu

The prevalence of relation networks in computer vision is in stark contrast to underexplored point-based methods. In this paper, we explore the possibilities of local relation operators and survey their feasibility. We propose a scalable and efficient module, called group relation aggregator. The module computes a feature of a group based on the aggregation of the features of the inner-group points weighted by geometric relations and semantic relations. We adopt this module to design our RPNet. We further verify the expandability of RPNet, in terms of both depth and width, on the tasks of classification and segmentation. Surprisingly, empirical results show that wider RPNet fits for classification, while deeper RPNet works better on segmentation. RPNet achieves state-of-the-art for classification and segmentation on challenging benchmarks. We also compare our local aggregator with PointNet++, with around 30% parameters and 50% computation saving. Finally, we conduct experiments to reveal the robustness of RPNet with regard to rigid transformation and noises.

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hancyran/RPNet officialmentioned on GitHubnot reachable when probed 2026-09-17 — repositories for recent papers often appear after camera-ready report

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Tasks

3D Classification 3D Point Cloud Classification 3D Semantic Segmentation Classification Segmentation Semantic Segmentation

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Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
3D Point Cloud Classification	ModelNet40	RPNet	Overall Accuracy	94.1	#22 of 111	Archive leaderboard	report
Semantic Segmentation	S3DIS	RPNet	Mean IoU	70.8	#22 of 54	Archive leaderboard	report
Semantic Segmentation	S3DIS	RPNet	Number of params	N/A	#22 of 54	Archive leaderboard	report
Semantic Segmentation	ScanNet	RPNet	test mIoU	68.2	#35 of 45	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.

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