FApSH: An effective and robust local feature descriptor for 3D registration and object recognition

• Published in: Pattern recognition Vol. 151; p. 110354
• Main Authors: Zhao, Bao, Wang, Zihan, Chen, Xiaobo, Fang, Xianyong, Jia, Zhaohong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2024
• Subjects: 3D feature description; 3D registration; Local reference axis; Point cloud; Local reference axis; Point cloud; 3D registration; 3D feature description
• ISSN: 0031-3203
• DOI: 10.1016/j.patcog.2024.110354

•A novel local feature descriptor called FApSH is proposed. It fully encodes the relevancy of the five attributes at each neighbor point based on an LRA, presenting strong robustness and high descriptiveness.•A priori distribution-based partition method is proposed, which can significantly reduce the length and also validly improve the descriptiveness of FApSH.•A radial distance-based histogram assignment method is proposed, which can enhance the robustness of FApSH.•The priori distribution-based partition and radial distance-based histogram assignment methods can be applied to other descriptors for improving their performance. Three-dimensional (3D) local feature descriptor plays an important role in 3D computer vision because it is widely used to build point-to-point correspondences in many 3D vision applications. However, existing descriptors are difficult to have both high descriptiveness and strong robustness to various nuisances (e.g., noise and occlusion). To address this problem, a descriptor named Fully Attribute-pairs Statistical Histogram (FApSH) is proposed. FApSH is constructed on a local reference axis (LRA), and fully encodes the relevancy information of five attributes at each neighbor point by ten attribute-pair statistics. In this process, a priori distribution-based partition strategy is proposed for evenly distributing the attribute values of all neighbor points, and a radial distance-based histogram assignment method is proposed to improve the robustness to noise and outliers. The proposed methods are rigorously evaluated on six benchmark datasets with different application scenarios and nuisances. The results show that FApSH has high descriptiveness and strong robustness. It obviously outperforms the existing handcrafted descriptors, and is comparable to some superior learning-based descriptors. The results also show that the proposed priori distribution-based partition strategy significantly reduces the length and also improves the descriptiveness of FApSH, and the proposed radial distance-based histogram assignment method improves the robustness of FApSH on various datasets.