Self-Similarity Based Compression of Point Clouds, with Apllication to Ray Tracing

Abstract

Many real-world, scanned surfaces contain repetitive structures, like bumps, ridges, creases, and so on. We present a compression technique that exploits self-similarity within a point-sampled surface. Our method replaces similar surface patches with an instance of a representative patch. We use a concise shape descriptor to identify and cluster similar patches. Decoding is achieved through simple instancing of the representative patches. Encoding is efficient, and can be applied to large datasets consisting of millions of points. Moreover, our technique offers random access to the compressed data, making it applicable to ray tracing, and easily allows for storing additional point attributes, like normals.