Feature Adaptive Surfel Set Simplification

Abstract

In this paper we propose a novel scheme for simplifying a surfel set with the resultant surfels computed and distributed in an effort to preserve prominent geometric and textural features with a reduced number of primitives. It works by iteratively collapsing local neighborhoods around surfels until a desired data reduction ratio is reached. The local neighborhood collapses are prioritized according to a cost metric that takes into account the local complexities of both the geometric and the textural information. Experimental results demonstrate that, besides yielding outstanding model quality at a reduced model size, the proposed scheme provides the flexibility for the users to specify the relative priorities in the geometric and the textural fidelity.