Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2003 | Symposium on Geometry Processing | statistical point geometry,direct rendering,point geometry,point sample,point model,pca analysis,current scheme,point sample geometry,statistical analysis,input model,hierarchical principal component analysis,principal component analysis,probability distribution |
Field | DocType | ISBN |
Bottleneck,Computer science,Theoretical computer science,Probability distribution,Artificial intelligence,Point (geometry),Computer vision,Graphics pipeline,Vertex (geometry),Algorithm,Bandwidth (signal processing),Rendering (computer graphics),Principal component analysis | Conference | 1-58113-687-0 |
Citations | PageRank | References |
22 | 1.19 | 30 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aravind Kalaiah | 1 | 96 | 4.10 |
| Amitabh Varshney | 2 | 1704 | 172.25 |