  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open surface rendering, where the in/out test is no longer valid, we present a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.
