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.

翻译：本研究提出了一种高精度、高效且具有物理机制的三维点云配准方法，该方法在众多重要三维视觉问题中占据核心地位。与现有仅考虑空间点信息而忽略表面几何结构的物理方法不同，我们探索了具有几何感知的刚体动力学来调节粒子（点）运动，从而实现了更精确鲁棒的配准。所提方法包含四个主要模块：首先，利用图信号处理框架定义一种新型签名（即每个点的响应强度），通过该签名成功描述了局部表面变化、重采样关键点并区分不同粒子；其次，针对现有物理方法对离群点敏感的问题，将定义的响应强度适配于鲁棒统计中的中位绝对偏差，并采用X84原则进行自适应离群点抑制，确保配准的鲁棒性与稳定性；随后，提出一种刚体变换下新的几何不变量以融合点云的高阶特征，并将其嵌入到力建模中，以可信地指导成对扫描数据间的对应关系；最后，引入自适应模拟退火方法搜索全局最优解，显著加速配准过程。我们在从距离扫描仪到激光雷达的多种数据集上开展综合实验以评估所提方法。结果表明，本方法在精度上优于代表性前沿方法，更适合大规模点云配准，且其速度与鲁棒性均显著优于多数现有竞争方法。