Volumetric shape representations have become ubiquitous in multi-view reconstruction tasks. They often build on regular voxel grids as discrete representations of 3D shape functions, such as SDF or radiance fields, either as the full shape model or as sampled instantiations of continuous representations, as with neural networks. Despite their proven efficiency, voxel representations come with the precision versus complexity trade-off. This inherent limitation can significantly impact performance when moving away from simple and uncluttered scenes. In this paper we investigate an alternative discretization strategy with the Centroidal Voronoi Tesselation (CVT). CVTs allow to better partition the observation space with respect to shape occupancy and to focus the discretization around shape surfaces. To leverage this discretization strategy for multi-view reconstruction, we introduce a volumetric optimization framework that combines explicit SDF fields with a shallow color network, in order to estimate 3D shape properties over tetrahedral grids. Experimental results with Chamfer statistics validate this approach with unprecedented reconstruction quality on various scenarios such as objects, open scenes or human.

翻译：体积形状表示在多视图重建任务中已变得无处不在。它们通常建立在规则体素网格之上，作为三维形状函数（如SDF或辐射场）的离散表示，既可以作为完整的形状模型，也可以作为连续表示（如神经网络）的采样实例。尽管体素表示已被证明具有高效性，但其精度与复杂度之间存在权衡。当处理复杂且杂乱的场景时，这种固有局限会显著影响性能。本文研究了基于质心Voronoi剖分（CVT）的替代离散化策略。CVT能够根据形状占据情况更好地划分观测空间，并将离散化集中在形状表面附近。为将这种离散化策略应用于多视图重建，我们提出了一种体积优化框架，该框架将显式SDF场与浅层色彩网络相结合，以在四面体网格上估计三维形状属性。通过Chamfer距离统计的实验结果验证了该方法在物体、开放场景及人体等多种情境下实现了前所未有的重建质量。