用球谐函数+参考点生成任意复杂形状，精度与速度双优。
SHARC: Reference point driven Spherical Harmonic Representation for Complex Shapes
- 以最优参考点为中心，用球谐函数表示距离场来建模形状。
- 重建误差更低，速度比现有方法快，且模型参数少。
- 适合需要高保真几何生成的3D建模与逆向工程场景。
我们提出SHARC，一种通过一系列球谐（SH）表示的距离场合成任意、无关亏格形状的新框架。这些距离场以内部最优位置的参考点为锚点，最大化对表面细节的学习。为此，我们设计了联合优化稀疏性、中心性和可见性的代价函数。每个参考点通过射线投射采样其到表面的可见距离场，并利用快速球谐变换（FSHT）计算球谐系数。为提升几何保真度，对系数施加可配置低通滤波，并基于邻近性引入局部一致性约束进行精修。在与最先进方法的对比中，该方法在重建精度和时间效率上均表现更优，同时保持模型简洁。源代码已开源：https://github.com/POSE-Lab/SHARC。
原文摘要 · Abstract (English)
We propose SHARC, a novel framework that synthesizes arbitrary, genus-agnostic shapes by means of a collection of Spherical Harmonic (SH) representations of distance fields. These distance fields are anchored at optimally placed reference points in the interior volume of the surface in a way that maximizes learning of the finer details of the surface. To achieve this, we employ a cost function that jointly maximizes sparsity and centrality in terms of positioning, as well as visibility of the surface from their location. For each selected reference point, we sample the visible distance field to the surface geometry via ray-casting and compute the SH coefficients using the Fast Spherical Harmonic Transform (FSHT). To enhance geometric fidelity, we apply a configurable low-pass filter to the coefficients and refine the output using a local consistency constraint based on proximity. Evaluation of SHARC against state-of-the-art methods demonstrates that the proposed method outperforms existing approaches in both reconstruction accuracy and time efficiency without sacrificing model parsimony. The source code is available at https://github.com/POSE-Lab/SHARC.
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