B-spline Surfaces of Clustered Point Sets with Normal Maps

In this paper, we propose a novel method that represents the highly-complex point sets by clustering the points to normal-mapped B-spline surfaces (NBSs). The main idea is to construct elaborate normal maps on simple surfaces for the realistic rendering of complex point-set models. Based on this obs...

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Bibliographic Details
Main Authors: Yanci Zhang, Hanqiu Sun, Enhua Wu
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Computer science
Frequency
Genetics
Sampling methods
Signal processing algorithms
Spline
Sun
Surface fitting
Surface reconstruction
Online Access:Request full text
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Summary:In this paper, we propose a novel method that represents the highly-complex point sets by clustering the points to normal-mapped B-spline surfaces (NBSs). The main idea is to construct elaborate normal maps on simple surfaces for the realistic rendering of complex point-set models. Based on this observation, we developed the coarse, normal-mapped B-spline surfaces to approximate the original point datasets with fine surface details. In our algorithm, a genetic clustering algorithm is proposed to automatically segment the point samples into several clusters according to their statistical properties, and a network of B-spline patches with normal maps are constructed according to the clustering results. Our experimental results show that this representation facilitates the modeling and rendering of complex point sets without losing the visual qualities.
DOI:10.1109/CADCG.2007.4407856