SHARC: Reference point driven Spherical Harmonic Representation for Complex Shapes

TL;DR

SHARC introduces a spherical harmonic-based framework for synthesizing complex, genus-agnostic shapes by optimally placing reference points and refining surface details, achieving high accuracy and efficiency.

Contribution

The paper presents a novel spherical harmonic representation method for complex shape synthesis using optimized reference points and a new refinement process.

Findings

Outperforms state-of-the-art methods in reconstruction accuracy.

Achieves faster processing times without losing model detail.

Provides a parsimonious shape representation.

Abstract

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.