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

Bao Zhao, Zihan Wang, Xiaobo Chen, Xianyong Fang, Zhaohong Jia

Abstract: Highlights•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.