Sparse RBF surface representations

In this paper, we propose a sparse surface representation for arbitrary surface models (point clouds, mesh models, continuous surface models, etc.). We approximate the input surface model with radial basis functions (RBF) whose centers are located on the medial axis of the input object surface. The sparsity of the RBF representation is achieved by solving an L1 optimization problem. Experimental results demonstrate that our method needs much less number of parameters to represent the input surface model with good accuracy. The sparse representation is useful in various applications, such as saving memory space in storing the surface models, and saving time in transmission of the surface models on the internet.