论文标题
Neuraludf：学习无符号距离字段，用于用任意拓扑的表面进行多视图重建
NeuralUDF: Learning Unsigned Distance Fields for Multi-view Reconstruction of Surfaces with Arbitrary Topologies
论文作者
论文摘要
我们提出了一种新的方法，称为Neuraludf，用于通过音量渲染从2D图像中重建具有任意拓扑的表面。基于神经渲染的重建的最新进展取得了令人信服的结果。但是，这些方法仅限于具有闭合表面的对象，因为它们采用签名距离函数（SDF）作为表面表示，这需要将目标形状分为内部和外部。在本文中，我们建议将表面表示为无符号距离函数（UDF），并开发一种新的卷渲染方案来学习神经UDF表示。具体而言，引入了将UDF属性与音量渲染方案相关联的新密度函数，以实现UDF字段的强大优化。 DTU和DeepFashion3D数据集上的实验表明，我们的方法不仅可以具有复杂类型的非关闭形状重建，而且还可以在基于SDF的方法上实现与基于SDF的方法相当的性能。
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.
