Enhancing point cloud analysis via neighbor aggregation correction based on cross-stage structure correlation

2025-06-18

AI Summary

Point cloud neighborhood aggregation suffers from interference by irrelevant points and feature-level disconnection; existing geometry-encoding methods incur high computational overhead and exhibit poor noise robustness. To address this, we propose a cross-stage structural correlation-driven neighbor aggregation correction framework. Specifically, we design a Point Distribution Set Abstraction (PDSA) module to model distributional correlations in high-dimensional space for aggregation refinement. We further introduce a lightweight cross-stage structural descriptor coupled with a keypoint mechanism to enhance structural homogeneity while reducing computational cost. Our method integrates neighborhood variance suppression, inter-class separability enhancement, and optimized keypoint sampling. Extensive experiments demonstrate that it significantly outperforms state-of-the-art baselines on semantic segmentation and classification tasks, achieving superior generalization with fewer parameters. Ablation studies and visualizations validate both its effectiveness and mechanistic soundness.

Technology Category

• Computer Vision: Segmentation
• Search and Optimization: Distributed Search
• Machine Learning: Structured Learning

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Point cloud analysis is the cornerstone of many downstream tasks, among which aggregating local structures is the basis for understanding point cloud data. While numerous works aggregate neighbor using three-dimensional relative coordinates, there are irrelevant point interference and feature hierarchy gap problems due to the limitation of local coordinates. Although some works address this limitation by refining spatial description though explicit modeling of cross-stage structure, these enhancement methods based on direct geometric structure encoding have problems of high computational overhead and noise sensitivity. To overcome these problems, we propose the Point Distribution Set Abstraction module (PDSA) that utilizes the correlation in the high-dimensional space to correct the feature distribution during aggregation, which improves the computational efficiency and robustness. PDSA distinguishes the point correlation based on a lightweight cross-stage structural descriptor, and enhances structural homogeneity by reducing the variance of the neighbor feature matrix and increasing classes separability though long-distance modeling. Additionally, we introducing a key point mechanism to optimize the computational overhead. The experimental result on semantic segmentation and classification tasks based on different baselines verify the generalization of the method we proposed, and achieve significant performance improvement with less parameter cost. The corresponding ablation and visualization results demonstrate the effectiveness and rationality of our method. The code and training weight is available at: https://github.com/AGENT9717/PointDistribution

Problem

• Address irrelevant point interference in point cloud neighbor aggregation
• Reduce computational overhead in cross-stage structure modeling
• Improve robustness against noise in point cloud feature distribution

Innovation

• Uses high-dimensional correlation for feature correction
• Lightweight cross-stage descriptor enhances structural homogeneity
• Key point mechanism optimizes computational overhead