NeUDF: Learning Neural Unsigned Distance Fields With Volume Rendering.

Abstract

Multi-view shape reconstruction has achieved impressive progresses thanks to the latest advances in the neural implicit 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 SDF-based neural renderer cannot scale to UDF, we formalize the rules of neural volume rendering for open surface reconstruction (e.g., self-consistent, unbiased, occlusion-aware), and derive a dedicated rendering weight function specially tailored for UDF. Furthermore, to cope with open surface rendering, where the in/out test is no longer valid, we present a dedicated normal regularization strategy to resolve the surface orientation ambiguity.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 two typical open surface datasets MGN (Bhatnagar et al., 2019) and Deep Fashion 3D (Zhu et al., 2020). Experimental results demonstrate that NeUDF can significantly outperform the state-of-the-art methods in the task of multi-view surface reconstruction, especially for the complex shapes with open boundaries.