PowerRTF: Power Diagram based Restricted Tangent Face for Surface Remeshing

Triangular meshes of superior quality are important for geometric processing in practical applications. Existing approximative CVT‐based remeshing methodology uses planar polygonal facets to fit the original surface, simplifying the computational complexity. However, they usually do not consider sur...

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Bibliographic Details
Published in:Computer graphics forum Vol. 42; no. 5
Main Authors: Yao, Yuyou, Liu, Jingjing, Fei, Yue, Wu, Wenming, Zhang, Gaofeng, Yan, Dong‐Ming, Zheng, Liping
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.08.2023
Subjects:
CCS Concepts
Centroids
Computing methodologies → Computer graphics
Curvature
Errors
Polygons
Shared memory algorithms
Topology
ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary:Triangular meshes of superior quality are important for geometric processing in practical applications. Existing approximative CVT‐based remeshing methodology uses planar polygonal facets to fit the original surface, simplifying the computational complexity. However, they usually do not consider surface curvature. Topological errors and outliers can also occur in the close sheet surface remeshing, resulting in wrong meshes. With this regard, we present a novel method named PowerRTF, an extension of the restricted tangent face (RTF) in conjunction with the power diagram, to better approximate the original surface with curvature adaption. The idea is to introduce a weight property to each sample point and compute the power diagram on the tangent face to produce area‐controlled polygonal facets. Based on this, we impose the variable‐capacity constraint and centroid constraint to the PowerRTF, providing the trade‐off between mesh quality and computational efficiency. Moreover, we apply a normal verification‐based inverse side point culling method to address the topological errors and outliers in close sheet surface remeshing. Our method independently computes and optimizes the PowerRTF per sample point, which is efficiently implemented in parallel on the GPU. Experimental results demonstrate the effectiveness, flexibility, and efficiency of our method.
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.14897