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:本研究为 3D 点云登记提供了一种高精度、高效和物理导引的方法,这是许多重要的 3D 视觉问题的核心。 与仅仅考虑空间点信息和忽略表面几何的基于物理的现有方法相比,我们探索几何认识僵硬体动态以调节粒子(点)运动,从而导致更精确和稳健的登记。 我们的拟议方法由四个主要模块组成。 首先,我们利用图形信号处理框架来定义一个新的信号(即每个点的点响应强度),通过它我们成功地描述当地表面变异、重新校验关键点和区分不同粒子。 然后,为了解决当前基于物理的、对外部体敏感的方法的缺点,我们探索了在稳健的统计中中度绝对偏差(MAD)的定点反应强度,并采纳了适应外部偏差的 X84 原则,确保了稳健和稳定的登记。 随后,我们提议在硬质变异性变中采用新型的测算,以纳入更精确的云点特征特征,进一步嵌入D 用于为最精准的模型, 指导最有代表性的阵势的阵势的阵势的阵势的阵势的阵势方法, 进行更快速的检索的搜索到更快速的系统, 。最后, 进行更快速的模拟的扫描式的测测算方法将更快速地对准的测算, 。最后,我们更快速的测算方法将演示定的测算方法将更快速地进行为我们进行更快速的测算, 。