GraphReg: Dynamical Point Cloud Registration with Geometry-aware Graph Signal Processing

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 intensity to median absolute deviation (MAD) in robust statistics and adopt the X84 principle for adaptive outlier depression, ensuring a robust and stable registration. Subsequently, we propose a novel geometric invariant under rigid transformations to incorporate higher-order features of point clouds, which is further embedded for force modeling to guide the correspondence between pairwise scans credibly. Finally, we introduce an adaptive simulated annealing (ASA) method to search for the global optimum and substantially accelerate the registration process. We perform comprehensive experiments to evaluate the proposed method on various datasets captured from range scanners to LiDAR. Results demonstrate that our proposed method outperforms representative state-of-the-art approaches in terms of accuracy and is more suitable for registering large-scale point clouds. Furthermore, it is considerably faster and more robust than most competitors.