Accelerating Surface Remeshing Through Gpu-Based Computation of the Restricted Tangent Face

Yao, Yuyou; Liu, Jingjing; Wu, Wenming; Zhang, Gaofeng; Xu, Benzhu; Zheng, Liping

doi:10.2139/ssrn.4177990

Cited by 1 publication

(17 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another option is to compute these approximative polygonal facets without auxiliary points directly [ZCHK13]. The tangent planes of sample points are utilized for surface approximation, e.g., RVC [CZC*18], RTF [YLW*23], etc. By restricting Voronoi cells on a set of best‐fitting planes, Chen et al [CZC*18] extend the CVT computation on point clouds and achieve partial surface curvature adaptation on the basis of a density‐dependent energy function, that is the density‐adapted RVC (D‐RVC).…”

Section: Related Workmentioning

confidence: 99%

“…By restricting Voronoi cells on a set of best‐fitting planes, Chen et al [CZC*18] extend the CVT computation on point clouds and achieve partial surface curvature adaptation on the basis of a density‐dependent energy function, that is the density‐adapted RVC (D‐RVC). More recently, Yao et al [YLW*23] present an RPF simplification, namely RTF, where the requirement for these shadow points is eliminated. They provide a remeshing method based on a 3D CVD framework [RSLL18] to produce meshes of high quality.…”

Section: Related Workmentioning

confidence: 99%

“…Let denote n different sample points on the original surface M . The RTF [YLW*23] defines a set of approximative polygonal facets to fit the original surface M . Each polygonal facet F i is determined on the basis of the tangent plane of s i .…”

Section: Preliminarymentioning

confidence: 99%

“…Exemplification of RTF and PowerRTF on the Eight model with same sample points, where purple dots represent the sample points on the surface. (a) RTFs of sample points in [YLW*23]; (b) triangular meshes generated by RTF [YLW*23]; (c) PowerRTFs of sample points to capture the surface curvature; and (d) triangular meshes obtained by PowerRTF.…”

Section: Preliminarymentioning

confidence: 99%

“…They are simple and straightforward, but the facet extraction relies on the 3D Voronoi diagram construction. Another option is to compute the clipped tangent planes of sample points to roughly resemble the surface, where auxiliary points and 3D Voronoi diagram construction are no longer necessary, such as the restricted Voronoi cell (RVC) [CZC*18] and the restricted tangent face (RTF) [YLW*23], etc. The computation of the clipped tangent planes for each sample point could be done independently, allowing for high parallelization and GPU implementation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

PowerRTF: Power Diagram based Restricted Tangent Face for Surface Remeshing

Yao

Liu

Fei

et al. 2023

Computer Graphics Forum

View full text Add to dashboard Cite

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.

show abstract