Statistical Point Geometry

Kalaiah, Aravind; Varshney, Amitabh

Statistical Point Geometry

Files

107-115.pdf (5.7 MB)

Date

2003

Authors

Kalaiah, Aravind
Varshney, Amitabh

Publisher

The Eurographics Association

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.

        @inproceedings{:10.2312/SGP/SGP03/107-115
,
booktitle = {Eurographics Symposium on Geometry Processing
},
editor = {Leif Kobbelt and Peter Schroeder and Hugues Hoppe
},
title = {{Statistical Point Geometry
}},
author = {Kalaiah, Aravind and 
Varshney, Amitabh
},
year = {2003
},
publisher = {The Eurographics Association
},
ISSN = {1727-8384
},
ISBN = {3-905673-06-1
},
DOI = {/10.2312/SGP/SGP03/107-115
}
}