Statistical geometry representation for efficient transmission and rendering

Traditional geometry representations have focused on representing the details of the geometry in a deterministic fashion. In this article we propose a statistical representation of the geometry that leverages local coherence for very large datasets. We show how the statistical analysis of a densely sampled point model can be used to improve the geometry bandwidth bottleneck, both on the system bus and over the network as well as for randomized rendering, without sacrificing visual realism. Our statistical representation is built using a clustering-based hierarchical principal component analysis (PCA) of the point geometry. It gives us a hierarchical partitioning of the geometry into compact local nodes representing attributes such as spatial coordinates, normal, and color. We pack this information into a few bytes using classification and quantization. This allows our representation to directly render from compressed format for efficient remote as well as local rendering. Our representation supports both view-dependent and on-demand rendering. Our approach renders each node using quasi-random sampling utilizing the probability distribution derived from the PCA analysis. We show many benefits of our approach: (1) several-fold improvement in the storage and transmission complexity of point geometry; (2) direct rendering from compressed data; and (3) support for local and remote rendering on a variety of rendering platforms such as CPUs, GPUs, and PDAs.