Parallelizable global conformal parameterization of simply-connected surfaces via partial welding
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Abstract (arXiv)
Conformal surface parameterization is useful in graphics, imaging and visualization, with applications to texture mapping, atlas construction, registration, remeshing and so on. With the increasing capability in scanning and storing data, dense 3D surface meshes are common nowadays. While meshes with higher resolution better resemble smooth surfaces, they pose computational difficulties for the existing parameterization algorithms. In this work, we propose a novel parallelizable algorithm for computing the global conformal parameterization of simply-connected surfaces via partial welding maps. A given simply-connected surface is first partitioned into smaller subdomains. The local conformal parameterizations of all subdomains are then computed in parallel. The boundaries of the parameterized subdomains are subsequently integrated consistently using a novel technique called partial welding, which is developed based on conformal welding theory. Finally, by solving the Laplace equation for each subdomain using the updated boundary conditions, we obtain a global conformal parameterization of the given surface, with bijectivity guaranteed by quasi-conformal theory. By including additional shape constraints, our method can be easily extended to achieve disk conformal parameterization for simply-connected open surfaces and spherical conformal parameterization for genus-0 closed surfaces. Experimental results are presented to demonstrate the effectiveness of our proposed algorithm. When compared to the state-of-the-art conformal parameterization methods, our method achieves a significant improvement in both computational time and accuracy.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Mar 2019.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1; 2025: 1; 2026: 1.
7 papers.
- 2021A unifying framework for n-dimensional quasi-conformal mappingscited by 7
- 2022Automatic Landmark Detection and Registration of Brain Cortical Surfaces via Quasi-Conformal Geometry and Convolutional Neural Networkscited by 5
- 2023Learning Continuous Mesh Representation with Spherical Implicit Surfacecited by 3
- 2021Adaptive area-preserving parameterization of open and closed anatomical surfacescited by 0
- 2024Gaussian Differentially Private Human Faces Under a Face Radial Curve Representationcited by 0
- 2025Variational Geometry-aware Neural Network based Method for Solving High-dimensional Diffeomorphic Mapping Problemscited by 0
- 2026An integrated geometric quantification and shape analysis framework for axillary lymph node metastasis in breast cancer patientscited by 0
- 2026An integrated geometric quantification and shape analysis framework for axillary lymph node metastasis in breast cancer patients28 Sep 2026
- 2025Variational Geometry-aware Neural Network based Method for Solving High-dimensional Diffeomorphic Mapping Problems31 Oct 2025
- 2024Gaussian Differentially Private Human Faces Under a Face Radial Curve Representation10 Sep 2024
- 2023Learning Continuous Mesh Representation with Spherical Implicit Surface11 Jan 2023
- 2022Automatic Landmark Detection and Registration of Brain Cortical Surfaces via Quasi-Conformal Geometry and Convolutional Neural Networks15 Aug 2022
- 2021Adaptive area-preserving parameterization of open and closed anatomical surfaces8 Nov 2021
- 2021A unifying framework for n-dimensional quasi-conformal mappings20 Oct 2021