  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.
