To accelerate the processing for the 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 applications. This paper proposes a saliency detection based points sampling method for mesh simplification. By generating and enhancing the saliency map, the regions which are visually important can be located. For the mesh simplification, the local details are captured by the saliency, 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. We present a number of results to show that the method significantly simplifies the surface without distortion and loss of local details.
['Yitian Zhao', 'Yonghuai Liu', 'Ran Song', 'Min Zhang']
A saliency detection based method for 3D surface simplification