The Quantized kd-Tree: Efficient Ray Tracing of Compressed Point Clouds

Abstract

Both ray tracing and point-based representations provide means to efficiently display very complex 3D models. Computational efficiency has been the main focus of previous work on ray tracing point-sampled surfaces. For very complex models efficient storage in the form of compression becomes necessary in order to avoid costly disk access. However, as ray tracing requires neighborhood queries, existing compression schemes cannot be applied because of their sequential nature. This paper introduces a novel acceleration structure called the quantized kd-tree, which offers both efficient traversal and storage. The gist of our new representation lies in quantizing the kd-tree splitting plane coordinates. We show that the quantized kd-tree reduces the memory footprint up to 18 times, not compromising performance. Moreover, the technique can also be employed to provide LOD (level-of-detail) to reduce aliasing problems, with little additional storage cost