Local and global point sampling for structured point cloud simplification

To accelerate the processing for integration, registration, representation and recognition of point cloud, it is of growing necessity to simplify the polygonal surface. Mesh simplification is an approach to vary the levels of visual details as appropriate, thereby improving on the overall performance of the applications. This paper proposes an effective mesh simplification method which is based on data points sampling. The sampling method considers both the local details and the overall shape. The local details analysis approach is based on graph-based segmentation, while the overall shape analysis, the approach voxelizes the model and samples points in terms of the entropy, based on the shape index of vertices. Like many mesh simplification methods, this approach reduces the number of vertices in a model. We present a number of results to show that the method simplifies the surface with local details and global shape.