Saliency map in 3D point cloud and its simplification application

A novel method of computing saliency map from 3D spatial information in point cloud data is proposed based on Gaussian normal vector estimation, in order to measure the regional importance for 3D point cloud data. The proposed method defines the salient critical points in a scalar function space using a center-surround filter operator on Gaussian-weighted average angle of normal vectors. We demonstrate the effectiveness of this approach by comparing our results with the results of the conventional approaches in a number of examples. Furthermore, this work has a variety of potential applications. In this paper, we present a direct simplification application in 3D point clouds based on visual saliency.