Visibility-Aware Progressive Farthest Point Sampling on the GPU

Abstract

In this paper, we present the first algorithm for progressive sampling of 3D surfaces with blue noise characteristics that runs entirely on the GPU. The performance of our algorithm is comparable to state-of-the-art GPU Poisson-disk sampling methods, while additionally producing ordered sequences of samples where every prefix exhibits good blue noise properties. The basic idea is, to reduce the 3D sampling domain to a set of 2.5D images which we sample in parallel utilizing the rasterization hardware of current GPUs. This allows for simple visibility-aware sampling that only captures the surface as seen from outside the sampled object, which is especially useful for point-based level-of-detail rendering methods. However, our method can be easily extended for sampling the entire surface without changing the basic algorithm. We provide a statistical analysis of our algorithm and show that it produces good blue noise characteristics for every prefix of the resulting sample sequence and analyze the performance of our method compared to related state-of-the-art sampling methods.