CUDA-based Signed Distance Field Calculation for Adaptive Grids

We present a simple yet robust signed distance field (SDF) generator based on recent GPU architectures. In our approach, the squared Euclidean distance is calculated for each triangle face in parallel, and then an optimized stream reduction process is used to find the shortest distance. During this process, the stream reduction operation acts like a parallel binary space-searching routine for each level. This process uses computations and memory bandwidth efficiently because of the massive number of CUDA threads. Signs are then determined by calculating angleweighted pseudonormals. Unlike some previous SDF approaches that only calculate the SDF near the surface or within the bounding box, our method can calculate the SDF adaptively so that there are no limitations on proximity or regularity. We also compare our GPU-based results with a kd-tree based single CPU approach for a 3D geometry synthesis application.