SIPF: Scale invariant point feature for 3D point clouds

摘要

In this paper, we propose a method for detecting Scale-Invariant Point Feature(SIPF) including 3D keypoints Detector and feature descriptor. To detect SIPF, we first estimate a keyscale for point cloud, and calculate the covariance matrix of each 3D point. Keypoints are the saliency points who have a fast change speed along with all principal directions. Then the descriptors are encoded based on the shape of a border or silhouette of an object to be detected or recognized. Experimental results with the Stanford datasets demonstrate that the proposed method can be effectively used for 3D point clouds expression.