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.

翻译：我们提出了SHARC，一种新颖的框架，通过一组距离场的球谐（SH）表示来合成任意、与亏格无关的形状。这些距离场锚定于表面内部体积中经最优放置的参考点，以最大化学习表面精细细节的能力。为实现这一目标，我们采用了一个代价函数，该函数在参考点定位方面联合最大化稀疏性和中心性，同时最大化从其位置观测表面的可见性。对于每个选定的参考点，我们通过光线投射对表面几何的可见距离场进行采样，并利用快速球谐变换（FSHT）计算SH系数。为增强几何保真度，我们对系数应用可配置的低通滤波器，并基于邻近性使用局部一致性约束对输出进行精炼。将SHARC与最先进方法的评估表明，所提出的方法在重建精度和时间效率上均优于现有方法，且不牺牲模型简洁性。源代码见 https://github.com/POSE-Lab/SHARC。