Point Cloud Registration Based on Multiple Neighborhood Feature Difference

Abstract

ABSTRACT Dense point cloud registration is a critical problem in computer vision and 3D reconstruction, with widespread applications in scenarios such as robotic navigation, autonomous driving, and 3D measurement. However, dense point cloud registration faces significant challenges, including high computational complexity and prolonged processing times. To address these issues, this paper proposes a point cloud registration method based on multiple neighborhood feature difference (MNFD) that employs a coarse‐to‐fine strategy to effectively enhance both registration efficiency and accuracy. The proposed method consists of two stages: coarse registration and fine registration. In the coarse registration stage, a novel feature point extraction approach based on MNFD is introduced, capable of identifying highly stable and distinctive feature points in the point cloud. These feature points are then utilized in combination with the fast point feature histogram (FPFH) algorithm to achieve an initial alignment between the target and template point clouds. In the fine registration stage, the results from the coarse alignment are refined using algorithms such as iterative closest point (ICP) to ensure both efficiency and precision during the registration process. Experiments conducted on publicly available datasets demonstrate the superiority of the proposed method compared to existing approaches.