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open surface rendering, where the in/out test is no longer valid, we present a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

NeUDF：利用体渲染学习神经无符号距离场

NeUDF: Leaning Neural Unsigned Distance Fields with Volume Rendering

We present a novel method, called NeuralUDF, for reconstructing surfaces with
arbitrary topologies from 2D images via volume rendering. Recent advances in
neural rendering based reconstruction have achieved compelling results.
However, these methods are limited to objects with closed surfaces since they
adopt Signed Distance Function (SDF) as surface representation which requires
the target shape to be divided into inside and outside. In this paper, we
propose to represent surfaces as the Unsigned Distance Function (UDF) and
develop a new volume rendering scheme to learn the neural UDF representation.
Specifically, a new density function that correlates the property of UDF with
the volume rendering scheme is introduced for robust optimization of the UDF
fields. Experiments on the DTU and DeepFashion3D datasets show that our method
not only enables high-quality reconstruction of non-closed shapes with complex
typologies, but also achieves comparable performance to the SDF based methods
on the reconstruction of closed surfaces.

本文提出 NeuralUDF 方法，通过体绘制从 2D 图像重建任意拓扑表面，使用无符号距离函数表示表面，成功实现复杂形态的非封闭形状高质量重建。

NeuralUDF: 学习无符号距离场进行任意拓扑表面的多视角重建

NeuralUDF: Learning Unsigned Distance Fields for Multi-view  Reconstruction of Surfaces with Arbitrary Topologies

Recent advances in learning 3D shapes using neural implicit functions have
achieved impressive results by breaking the previous barrier of resolution and
diversity for varying topologies. However, most of such approaches are limited
to closed surfaces as they require the space to be divided into inside and
outside. More recent works based on unsigned distance function have been
proposed to handle complex geometry containing both the open and closed
surfaces. Nonetheless, as their direct outputs are point clouds, robustly
obtaining high-quality meshing results from discrete points remains an open
question. We present a novel learnable implicit representation, called the
three-pole signed distance function (3PSDF), that can represent non-watertight
3D shapes with arbitrary topologies while supporting easy field-to-mesh
conversion using the classic Marching Cubes algorithm. The key to our method is
the introduction of a new sign, the NULL sign, in addition to the conventional
in and out labels. The existence of the null sign could stop the formation of a
closed isosurface derived from the bisector of the in/out regions. Further, we
propose a dedicated learning framework to effectively learn 3PSDF without
worrying about the vanishing gradient due to the null labels. Experimental
results show that our approach outperforms the previous state-of-the-art
methods in a wide range of benchmarks both quantitatively and qualitatively.

本文介绍了一种新的可学习的内隐表示方法称为三极符号距离函数（3PSDF），可以表示任意拓扑的非水密 3D 形状并使用经典的 Marching Cubes 算法进行易于场 - 网格转换。该方法引入了新符号 NULL 符号，支持处理开放的几何体表面。实验结果表明，该方法在定量和定性评估中都胜过以前的最先进方法。