10.2312/SGP/SGP03/107-115
Kalaiah, Aravind
Varshney, Amitabh
Statistical Point Geometry
The Eurographics Association
2003
Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture/Image Generation; I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling; G.3 [Mathematics of Computing]: Probability and Statistics
eng
1727-8384
3-905673-06-1
http://diglib.eg.org/EG/DL/WS/SGP/SGP03/107-115.pdf
9 pages
Eurographics Symposium on Geometry Processing
We propose a scheme for modeling point sample geometry with statistical analysis. In our scheme we depart from the current schemes that deterministically represent the attributes of each point sample. We show how the statistical analysis of a densely sampled point model can be used to improve the geometry bandwidth bottleneck and to do randomized rendering without sacrificing visual realism. We first carry out a hierarchical principal component analysis (PCA) of the model. This stage partitions the model into compact local geometries by exploiting local coherence. Our scheme handles vertex coordinates, normals, and color. The input model is reconstructed and rendered using a probability distribution derived from the PCA analysis. We demonstrate the benefits of this approach in all stages of the graphics pipeline: (1) orders of magnitude improvement in the storage and transmission complexity of point geometry, (2) direct rendering from compressed data, and (3) view-dependent randomized rendering.