A Retinex theory based points sampling method for mesh simplification

To accelerate the processing for integration, registration, representation and recognition of point clouds, it is of growing necessity to simplify the surface of 3-D models. 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 a Retinex theory based points sampling method for mesh simplification. The sampling method considers both the local details and the overall shape. The local details are captured by a graph-based segmentation, while for the overall shape, the approach voxelizes the model and samples points in terms of the entropy of the shape index of vertices in voxels. 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 significantly simplifies the surface without losing local details and global shape.