Parallel Loop Subdivision with Sparse Adjacency Matrix

Abstract

Subdivision surface is a popular technique for geometric modeling. Recently, several parallel implementations have been developed for Loop subdivision on the GPU. However, these methods are built on complex data structures which complicate the implementation and affect the performance, especially on the GPU. In this work, we propose to simply use the sparse adjacency matrix which enables us to implement the Loop subdivision scheme in the most straightforward manner. Our implementation run entirely on the GPU and achieves high performance in runtime with significantly lower memory consumption than the state-of-the-art. Through extensive experiments and comparisons, we demonstrate the efficacy and efficiency of our method.