← 研踪 PaperTrailarXiv:2611.00721 · v2
Representation-Aware Closed-Surface Parameterization: Exact Sensitivity Analysis and Pole-Aware Planar Preconditioning
首次提交 2026-10-31 15:49
版本更新 2026-11-03 22:50
雷达观测 2026-11-04 23:39
摘要
Surface parameterization is a fundamental task in geometry processing and shape analysis. For topologically closed surfaces, it is common to parameterize them onto a standardized closed domain in $\mathbb{R}^3$, with a prime example being a unit sphere for genus-0 closed surfaces. Many mapping methods use planar projections and perform computations on the plane rather than directly on the 3D surface. However, perturbations introduced before projection or geometric evolution propagate through the computation and may affect map quality, convergence, stopping behavior, and geometric validity. In this work, we formulate representation sensitivity as a branchwise input--output problem, separating smooth propagation through active Jacobians from changes in anchors, stopping rules, and fallback events. Under consistent piecewise-linear assembly, the unmodified south-pole Beltrami correction in the FLASH method for spherical conformal parameterization reduces exactly to source-mesh harmonic extension. On a fixed-anchor branch, the corrected map factors into chart transition, harmonic propagation, and inverse stereographic projection, yielding an explicit sensitivity Jacobian and a finite-triangle conditioning bound. Separately, we introduce a bounded five-parameter affine preconditioner over planar scale, translation, and anisotropy and evaluate candidates on the corrected spherical map. Across 30 genus-0 surfaces, it reduces median polar upper-tail log-condition and chord-angle distortion by $10.17\%$ and $10.97\%$, respectively, with both measures improving on every mesh. We also apply the framework to other spherical mapping methods including the conformalized mean-curvature flow and spherical density-equalizing maps. Altogether, our work highlights the importance of representation selection as a structural and practical component of spherical parameterization and downstream computation.
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v2 · 2026-11-03 22:50 · 最新观测版本
Representation-Aware Closed-Surface Parameterization: Exact Sensitivity Analysis and Pole-Aware Planar Preconditioning
Surface parameterization is a fundamental task in geometry processing and shape analysis. For topologically closed surfaces, it is common to parameterize them onto a standardized closed domain in $\mathbb{R}^3$, with a prime example being a unit sphere for genus-0 closed surfaces. Many mapping methods use planar projections and perform computations on the plane rather than directly on the 3D surface. However, perturbations introduced before projection or geometric evolution propagate through the computation and may affect map quality, convergence, stopping behavior, and geometric validity. In this work, we formulate representation sensitivity as a branchwise input--output problem, separating smooth propagation through active Jacobians from changes in anchors, stopping rules, and fallback events. Under consistent piecewise-linear assembly, the unmodified south-pole Beltrami correction in the FLASH method for spherical conformal parameterization reduces exactly to source-mesh harmonic extension. On a fixed-anchor branch, the corrected map factors into chart transition, harmonic propagation, and inverse stereographic projection, yielding an explicit sensitivity Jacobian and a finite-triangle conditioning bound. Separately, we introduce a bounded five-parameter affine preconditioner over planar scale, translation, and anisotropy and evaluate candidates on the corrected spherical map. Across 30 genus-0 surfaces, it reduces median polar upper-tail log-condition and chord-angle distortion by $10.17\%$ and $10.97\%$, respectively, with both measures improving on every mesh. We also apply the framework to other spherical mapping methods including the conformalized mean-curvature flow and spherical density-equalizing maps. Altogether, our work highlights the importance of representation selection as a structural and practical component of spherical parameterization and downstream computation.
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