标题： 可扩展的高质量神经隐式表示用于三维重建 显示英文标题

标题： Scalable and High-Quality Neural Implicit Representation for 3D Reconstruction

摘要： 基于SDF的神经隐式表面重建方法最近被提出，并展示了显著的建模能力。 然而，由于单一网络的全局特性和有限的表示能力，现有方法仍然存在许多缺点，例如重建的精度和规模有限。 在本文中，我们提出了一种通用、可扩展且高质量的神经隐式表示来解决这些问题。 我们将分而治之的方法集成到神经SDF-based重建中。 具体来说，我们将物体或场景建模为多个具有重叠区域的独立局部神经SDF的融合。 我们表示的构建涉及三个关键步骤：(1) 基于物体结构或数据分布构建局部辐射场的分布和重叠关系，(2) 相邻局部SDF的相对位姿配准，以及(3) SDF融合。 得益于每个局部区域的独立表示，我们的方法不仅可以实现高保真表面重建，还可以实现可扩展的场景重建。 大量的实验结果证明了我们所提出方法的有效性和实用性。 显示英文摘要

摘要： Various SDF-based neural implicit surface reconstruction methods have been proposed recently, and have demonstrated remarkable modeling capabilities. However, due to the global nature and limited representation ability of a single network, existing methods still suffer from many drawbacks, such as limited accuracy and scale of the reconstruction. In this paper, we propose a versatile, scalable and high-quality neural implicit representation to address these issues. We integrate a divide-and-conquer approach into the neural SDF-based reconstruction. Specifically, we model the object or scene as a fusion of multiple independent local neural SDFs with overlapping regions. The construction of our representation involves three key steps: (1) constructing the distribution and overlap relationship of the local radiance fields based on object structure or data distribution, (2) relative pose registration for adjacent local SDFs, and (3) SDF blending. Thanks to the independent representation of each local region, our approach can not only achieve high-fidelity surface reconstruction, but also enable scalable scene reconstruction. Extensive experimental results demonstrate the effectiveness and practicality of our proposed method.