Efficient and accurate registration with BWPH descriptor for low-quality point clouds

Abstract

Point cloud registration based on local descriptors plays a crucial role in 3D computer vision applications. However, existing methods often suffer from limitations such as low accuracy, a large memory footprint, and slow speed, particularly when dealing with 3D point clouds from low-cost sensors. To overcome these challenges, we propose an efficient local descriptor called Binary Weighted Projection-point Height (BWPH) for point cloud registration. The core idea behind the BWPH descriptor is the integration of Gaussian kernel density estimation with weighted height characteristics and binarization components to encode distinctive information for the local surface. Through extensive experiments and rigorous comparisons with state-of-the-art methods, we demonstrate that the BWPH descriptor achieves high matching accuracy, strong compactness, and feasibility across contexts. Moreover, the proposed BWPH-based point cloud registration successfully registers real datasets acquired by low-cost sensors with small errors, enabling accurate initial alignment positions.