In recent years, neural implicit representations gained popularity in 3D reconstruction due to their expressiveness and flexibility. However, the implicit nature of neural implicit representations results in slow inference time and requires careful initialization. In this paper, we revisit the classic yet ubiquitous point cloud representation and introduce a differentiable point-to-mesh layer using a differentiable formulation of Poisson Surface Reconstruction (PSR) that allows for a GPU-accelerated fast solution of the indicator function given an oriented point cloud. The differentiable PSR layer allows us to efficiently and differentiably bridge the explicit 3D point representation with the 3D mesh via the implicit indicator field, enabling end-to-end optimization of surface reconstruction metrics such as Chamfer distance. This duality between points and meshes hence allows us to represent shapes as oriented point clouds, which are explicit, lightweight and expressive. Compared to neural implicit representations, our Shape-As-Points (SAP) model is more interpretable, lightweight, and accelerates inference time by one order of magnitude. Compared to other explicit representations such as points, patches, and meshes, SAP produces topology-agnostic, watertight manifold surfaces. We demonstrate the effectiveness of SAP on the task of surface reconstruction from unoriented point clouds and learning-based reconstruction.

翻译：近年来,由于3D重建的清晰度和灵活性,神经隐含的表示在3D重建中越来越受欢迎,然而,神经隐含的表示的隐含性质导致缓慢的推断时间,需要仔细初始化。在本文件中,我们重新审视经典的但无处不在的云的云的表示,并采用不同的Poisson地表重建(PSR)配方,允许GPU加速快速解析指标功能,给取方向云层。不同的PSR层使我们能够通过隐含指标字段,高效率和差异化地将明确的3D点表示与3D网格的表示相衔接起来,从而能够优化诸如Chamfer距离等地表重建指标的端到端优化。因此,各点和模层之间的这种双重性使我们能够将形状表现为定向的点云层云,这些云层是清晰的、轻量度和直观的。与神经隐含的表示相比,我们的SAP-A-点模型(SAP)模型更易解、轻度,并且以一个程度的顺序加速推推推导时间。我们将SAP-SAP-Spedrodal-deal-resmagraphy-ressim-simp-sign-sign-imp-simpilling supdudududududududududustr。