Point cloud resampling using centroidal Voronoi tessellation methods
| Autor: | Juan Cao, Yongjie Jessica Zhang, Zhonggui Chen, Cheng Wang, Tieyi Zhang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Interleaving
Computer science Isotropy ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Point cloud 020207 software engineering 02 engineering and technology 01 natural sciences Computer Graphics and Computer-Aided Design Industrial and Manufacturing Engineering Statistics::Computation 0104 chemical sciences Computer Science Applications Smooth surface 010404 medicinal & biomolecular chemistry Robustness (computer science) Resampling 0202 electrical engineering electronic engineering information engineering Centroidal Voronoi tessellation Voronoi diagram Algorithm ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | Computer-Aided Design. 102:12-21 |
| ISSN: | 0010-4485 |
| Popis: | This paper presents a novel technique for resampling point clouds of a smooth surface. The key contribution of this paper is the generalization of centroidal Voronoi tessellation (CVT) to point cloud datasets to make point resampling practical and efficient. In particular, the CVT on a point cloud is efficiently computed by restricting the Voronoi cells to the underlying surface, which is locally approximated by a set of best-fitting planes. We also develop an efficient method to progressively improve the resampling quality by interleaving optimization of resampling points and update of the fitting planes. Our versatile framework is capable of generating high-quality resampling results with isotropic or anisotropic distributions from a given point cloud. We conduct extensive experiments to demonstrate the efficacy and robustness of our resampling method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect