Recent progress in neural implicit functions has set new state-of-the-art in reconstructing high-fidelity 3D shapes from a collection of images. However, these approaches are limited to closed surfaces as they require the surface to be represented by a signed distance field. In this paper, we propose NeAT, a new neural rendering framework that can learn implicit surfaces with arbitrary topologies from multi-view images. In particular, NeAT represents the 3D surface as a level set of a signed distance function (SDF) with a validity branch for estimating the surface existence probability at the query positions. We also develop a novel neural volume rendering method, which uses SDF and validity to calculate the volume opacity and avoids rendering points with low validity. NeAT supports easy field-to-mesh conversion using the classic Marching Cubes algorithm. Extensive experiments on DTU, MGN, and Deep Fashion 3D datasets indicate that our approach is able to faithfully reconstruct both watertight and non-watertight surfaces. In particular, NeAT significantly outperforms the state-of-the-art methods in the task of open surface reconstruction both quantitatively and qualitatively.

翻译：摘要：神经隐式函数的最新进展在从图像集合重建高保真3D形状方面取得了新的最优成果。然而，这些方法因要求曲面由符号距离场表示而仅限于闭合曲面。本文提出NeAT——一种新的神经渲染框架，能够从多视图图像学习任意拓扑结构的隐式曲面。具体而言，NeAT将3D曲面表示为符号距离函数（SDF）的水平集，并附带一个有效性分支，用于估计查询位置处的曲面存在概率。我们还开发了一种新颖的神经体渲染方法，利用SDF和有效性计算体不透明度，并避免渲染有效性低的点。NeAT支持使用经典Marching Cubes算法轻松将场转换为网格。在DTU、MGN和Deep Fashion 3D数据集上的大量实验表明，我们的方法能够忠实地重建水密和非水密曲面。特别地，NeAT在开放曲面重建任务中无论定量还是定性均显著优于现有最优方法。