Random Walk on Point Clouds for Feature Detection

2026-04-22

AI Summary

This study addresses the challenge of keypoint detection in point clouds by proposing a two-stage context-aware approach. It first introduces a Disk Sampling Neighborhood (DSN) descriptor designed to preserve local neighborhood structure, then constructs a graph that integrates spatial distribution, topological relationships, and geometric features. On this unified graph representation, a random walk strategy (RWoDSN) is employed to identify salient keypoints. Notably, this work is the first to jointly incorporate these three complementary sources of information within a graph-based random walk framework, thereby overcoming limitations inherent in conventional geometry-only invariant methods. Experimental results demonstrate significant performance gains across eight evaluation metrics, achieving a recall of 0.769—representing up to a 22% improvement—and a precision of 0.784, outperforming existing state-of-the-art techniques.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Graph-based Machine Learning
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

The points on the point clouds that can entirely outline the shape of the model are of critical importance, as they serve as the foundation for numerous point cloud processing tasks and are widely utilized in computer graphics and computer-aided design. This study introduces a novel method, RWoDSN, for extracting such feature points, incorporating considerations of sharp-to-smooth transitions, large-to-small scales, and textural-to-detailed features. We approach feature extraction as a two-stage context-dependent analysis problem. In the first stage, we propose a novel neighborhood descriptor, termed the Disk Sampling Neighborhood (DSN), which, unlike traditional spatially and geometrically invariant approaches, preserves a matrix structure while maintaining normal neighborhood relationships. In the second stage, a random walk is performed on the DSN (RWoDSN), yielding a graph-based DSN that simultaneously accounts for the spatial distribution, topological properties, and geometric characteristics of the local surface surrounding each point. This enables the effective extraction of feature points. Experimental results demonstrate that the proposed RWoDSN method achieves a recall of 0.769-22% higher than the current state-of-the-art-alongside a precision of 0.784. Furthermore, it significantly outperforms several traditional and deep-learning techniques across eight evaluation metrics.

Problem

• point cloud
• feature detection
• shape representation
• geometric features
• computer graphics

Innovation

• Random Walk
• Point Cloud Feature Detection
• Disk Sampling Neighborhood
• Graph-based Representation
• Context-dependent Analysis