GraphReg: Dynamical Point Cloud Registration with Geometry-aware Graph Signal Processing
Zhao Mingyang, Ma Lei, Jia Xiaohong, Yan Dong-Ming, and Huang Tiejun

TL;DR
GraphReg introduces a geometry-aware, physics-inspired point cloud registration method that enhances accuracy, robustness, and speed by integrating local surface variation, outlier suppression, higher-order features, and global optimization.
Contribution
The paper proposes a novel registration framework combining graph signal processing, robust statistics, geometric invariants, and adaptive simulated annealing for improved 3D point cloud alignment.
Findings
Outperforms state-of-the-art methods in accuracy.
More robust to outliers and large-scale data.
Faster convergence and registration speed.
Abstract
This study presents a high-accuracy, efficient, and physically induced method for 3D point cloud registration, which is the core of many important 3D vision problems. In contrast to existing physics-based methods that merely consider spatial point information and ignore surface geometry, we explore geometry aware rigid-body dynamics to regulate the particle (point) motion, which results in more precise and robust registration. Our proposed method consists of four major modules. First, we leverage the graph signal processing (GSP) framework to define a new signature, (i.e., point response intensity for each point), by which we succeed in describing the local surface variation, resampling keypoints, and distinguishing different particles. Then, to address the shortcomings of current physics-based approaches that are sensitive to outliers, we accommodate the defined point response…
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Taxonomy
Topics3D Shape Modeling and Analysis · Advanced Neural Network Applications · Graph Theory and Algorithms
MethodsAttentive Walk-Aggregating Graph Neural Network
