A 3D Simplification Method based on Dual Point Sampling

Abstract

For efficiently processing integration, registration, representation and recognition of large-scale 3D point clouds stored in computer disks or other hardware, it is important to simplify their sheer volume, discarding redundant information and meanwhile preserving the most important information as much as possible. This paper proposes an effective point cloud simplification method which is based on data points sampling. The dual sampling scheme considers both the local details and the overall shape. The local details analysis approach is based on graph-based segmentation, while for the overall shape analysis, the approach voxelizes the model and samples points in terms of the entropy, based on the shape index of vertices. Compared to other simplification methods, this approach reduces the number of vertices in a 3D model described by a point cloud and better preserves local details. We present a number of results to show that the method performs well both visually and quantitatively.