Visibility computation was crucial for computer graphics from its very beginning. The first visibility algorithms in computer graphics aimed to determine visible surfaces in a synthesized image of a three-dimensional scene. Nowadays there are many different visibility algorithms for various visibility problems. We propose a new taxonomy of visibility problems that is based on a classification according to the problem domain. We provide a broad overview of visibility problems and algorithms in computer graphics grouped by the proposed taxonomy. We survey visible surface algorithms, visibility culling algorithms, visibility algorithms for shadow computation, global illumination, point-based and image-based rendering, and global visibility computations. Finally, we discuss common concepts of visibility algorithm design and several criteria for the classification of visibility algorithms.

We propose a novel massively parallel construction algorithm for Bounding Volume Hierarchies (BVHs) based on locally-ordered agglomerative clustering. Our method builds the BVH iteratively from bottom to top by merging a batch of cluster pairs in each iteration. To efficiently find the neighboring clusters, we keep the clusters ordered along the Morton curve. This ordering allows us to identify approximate nearest neighbors very efficiently and in parallel. We implemented our algorithm in CUDA and evaluated it in the context of GPU ray tracing. For complex scenes, our method achieves up to a twofold reduction of build times while providing up to 17 percent faster trace times compared with the state-of-the-art methods.