Both real-valued and binary multi-feature fusion histograms for 3D local shape representation

3D Local feature description is an essential work in 3D computer vision since a lot of downstream techniques rely on point-to-point correspondences. Most of the existing descriptors perform the description on local surfaces from one single aspect, which inherently results in limited performance. So this paper first proposes a real-valued 3D local feature descriptor named multi-feature fusion histogram (MFFH), which combines five different types of well-designed geometric features to achieve comprehensive descriptions for local surfaces. In addition, to be available for platforms with limited computing and storage resources, we conduct a seamless extension of MFFH to its binary representation B-MFFH by three kinds of directed binarization methods toward different real-valued features. Through extensive evaluation experiments on the benchmark datasets, we prove the superiorities of the proposed MFFH and B-MFFH concerning comprehensive performance. Lastly, the practicability of the proposed MFFH and B-MFFH descriptors is visually demonstrated by the point cloud registration experiments.