Spherical Density-Equalizing Map for Genus-0 Closed Surfaces
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Abstract (arXiv)
Density-equalizing maps are a class of mapping methods in which the shape deformation is driven by prescribed density information. In recent years, they have been widely used for data visualization on planar domains and planar parameterization of open surfaces. However, the theory and computation of density-equalizing maps for closed surfaces are much less explored. In this work, we develop a novel method for computing spherical density-equalizing maps for genus-0 closed surfaces. Specifically, we first compute a conformal parameterization of the given genus-0 closed surface onto the unit sphere. Then, we perform density equalization on the spherical domain based on the given density information to achieve a spherical density-equalizing map. The bijectivity of the mapping is guaranteed using quasi-conformal theory. We further propose a method for incorporating the harmonic energy and landmark constraints into our formulation to achieve landmark-aligned spherical density-equalizing maps balancing different distortion measures. Using the proposed methods, a large variety of spherical parameterizations can be achieved. Applications to surface registration, remeshing, and data visualization are presented to demonstrate the effectiveness of our methods.
Key facts. Cited by 9 papers in the map (data through 13 Oct 2026). First posted on arXiv 22 Jan 2024.
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Citing papers per year: 2024: 1; 2025: 3; 2026: 5.
9 papers.
- 2025Learning-based density-equalizing mapcited by 2
- 2024Shape Prior Segmentation Guided by Harmonic Beltrami Signaturecited by 1
- 2025Free-Boundary Quasiconformal Maps via a Least-squares Operator in Diffeomorphism Optimizationcited by 1
- 2025Variational Geometry-aware Neural Network based Method for Solving High-dimensional Diffeomorphic Mapping Problemscited by 0
- 2026Two-chart Beltrami Optimization for Distortion-Controlled Spherical Bijection with Application to Brain Surface Registrationcited by 0
- 2026Harmonic Beltrami Signature Network: a Shape Prior Module in Deep Learning Frameworkcited by 0
- 2026Resolution-free neural surrogates for geometric parameterization and mapping with spatially varying fieldscited by 0
- 2026Learning Task-Aware Sampling with Shared Saliency through Density-Equalizing Mappingscited 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
- 2026Learning Task-Aware Sampling with Shared Saliency through Density-Equalizing Mappings11 Jun 2026
- 2026Resolution-free neural surrogates for geometric parameterization and mapping with spatially varying fields27 May 2026
- 2026Harmonic Beltrami Signature Network: a Shape Prior Module in Deep Learning Framework3 Mar 2026
- 2026Two-chart Beltrami Optimization for Distortion-Controlled Spherical Bijection with Application to Brain Surface Registration2 Feb 2026
- 2025Free-Boundary Quasiconformal Maps via a Least-squares Operator in Diffeomorphism Optimization12 Nov 2025
- 2025Variational Geometry-aware Neural Network based Method for Solving High-dimensional Diffeomorphic Mapping Problems31 Oct 2025
- 2025Learning-based density-equalizing map10 Jun 2025
- 2024Shape Prior Segmentation Guided by Harmonic Beltrami Signature5 Jul 2024