SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Parametrization and smooth approximation of surface triangulations
Computer Aided Geometric Design
MAPS: multiresolution adaptive parameterization of surfaces
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Consistent mesh parameterizations
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Spherical parametrization and remeshing
ACM SIGGRAPH 2003 Papers
Fundamentals of spherical parameterization for 3D meshes
ACM SIGGRAPH 2003 Papers
Spanning Tree Seams for Reducing Parameterization Distortion of Triangulated Surfaces
SMI '02 Proceedings of the Shape Modeling International 2002 (SMI'02)
SMI '04 Proceedings of the Shape Modeling International 2004
Cross-parameterization and compatible remeshing of 3D models
ACM SIGGRAPH 2004 Papers
ACM SIGGRAPH 2004 Papers
ACM SIGGRAPH 2005 Papers
Mesh parameterization: theory and practice
ACM SIGGRAPH ASIA 2008 courses
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Manifold parameterization considers the problem of parameterizing a given triangular mesh onto another mesh surface, which could be particularly plane or sphere surfaces. In this paper we propose a unified framework for manifold parameterization between arbitrary meshes with identical genus. Our approach does this task by directly mapping the connectivity of the source mesh onto the target mesh surface without any intermediate domain and partition of the meshes. The connectivity graph of source mesh is used to approximate the geometry of target mesh using least squares meshes. A subset of user specified vertices are constrained to have the geometry information of the target mesh. The geometry of the mesh vertices is reconstructed while approximating the known geometry of the subset by positioning each vertex approximately at the center of its immediate neighbors. This leads to a sparse linear system which can be effectively solved. Our approach is simple and fast with less user interactions. Many experimental results and applications are presented to show the applicability and flexibility of the approach.