Multi-view shape reconstruction has achieved impressive progresses thanks to
the latest advances in neural implicit surface rendering. However, existing
methods based on signed distance function (SDF) are limited to closed surfaces,
failing to reconstruct a wide range of real-world objects that contain
open-surface structures. In this work, we introduce a new neural rendering
framework, coded NeUDF, that can reconstruct surfaces with arbitrary topologies
solely from multi-view supervision. To gain the flexibility of representing
arbitrary surfaces, NeUDF leverages the unsigned distance function (UDF) as
surface representation. While a naive extension of an SDF-based neural renderer
cannot scale to UDF, we propose two new formulations of weight function
specially tailored for UDF-based volume rendering. Furthermore, to cope with
open surface rendering, where the in/out test is no longer valid, we present a
dedicated normal regularization strategy to resolve the surface orientation
ambiguity. We extensively evaluate our method over a number of challenging
datasets, including DTU}, MGN, and Deep Fashion 3D. Experimental results
demonstrate that nEudf can significantly outperform the state-of-the-art method
in the task of multi-view surface reconstruction, especially for complex shapes
with open boundaries.

本文介绍了一种新的神经渲染框架，名为 nEudf，它可以仅通过多视图监督来重建具有任意拓扑结构的表面；多次实验结果表明，相对于已有的多视图表面重建方法，nEudf 在重建形状复杂、具有开放边界的物体时表现更卓越。