An efficient and compact 3D local descriptor based on the weighted height image

Abstract

3D local descriptors are the fundamental and essential elements that have been commonly applied in 3D computer vision. This paper proposes a novel and effective 3D local descriptor for describing the 3D local shape. The research focuses on accelerating the descriptor generation by simplifying the Local Reference Frame (LRF) and optimizing the feature space through a Weighted Height Image (WHI). An in-depth theoretical analysis of the LRF is conducted. Then, this study proposes a simplified LRF to reduce the redundant computations of the covariance matrix and share the calculations with the 3D information coding. Besides, the feature space is modeled and analyzed in this paper. Based on the analysis, we propose a weighting function to strengthen the abilities of the feature representation. The experimental results indicate that the proposed WHI descriptor outperforms the state-of-the-art (SOTA) algorithms in terms of accuracy and efficiency. Meanwhile, the compactness of the WHI is about six times more than that of the SOTA algorithms. Moreover, for the application of point cloud registration, the proposed WHI exhibits high effectiveness in terms of both accuracy and real-time capability.