HUSC: a local feature descriptor of point cloud based on hemisphere neighborhood

Abstract

In order to improve the efficiency of LiDAR point cloud object recognition and reduce the computational overhead, a new feature descriptor, Hemispheric Unique Shape Context (HUSC), is presented in this paper by using an improved neighborhood determination method. Firstly, the normal vector and tangent plane at key point are estimated and the local reference frame is established. Then a hemispherical neighborhood is constructed based on the tangent plane and divided into bins according to azimuth, polar angle and radial direction. Finally, the points in each bin are counted and the local feature descriptors of key points are obtained. HUSC feature descriptor can not only ensure the discriminability of descriptors, but also improve the efficiency of object recognition by reducing the number of free bins. Experiments on Bologna dataset and 3DMatch dataset show that HUSC feature descriptor with hemispheric neighborhood is robust to noise, occupying less memory and operating faster.