  Recently, building on the foundation of neural radiance field, various
techniques have emerged to learn unsigned distance fields (UDF) to reconstruct
3D non-watertight models from multi-view images. Yet, a central challenge in
UDF-based volume rendering is formulating a proper way to convert unsigned
distance values into volume density, ensuring that the resulting weight
function remains unbiased and sensitive to occlusions. Falling short on these
requirements often results in incorrect topology or large reconstruction errors
in resulting models. This paper addresses this challenge by presenting a novel
two-stage algorithm, 2S-UDF, for learning a high-quality UDF from multi-view
images. Initially, the method applies an easily trainable density function
that, while slightly biased and transparent, aids in coarse reconstruction. The
subsequent stage then refines the geometry and appearance of the object to
achieve a high-quality reconstruction by directly adjusting the weight function
used in volume rendering to ensure that it is unbiased and occlusion-aware.
Decoupling density and weight in two stages makes our training stable and
robust, distinguishing our technique from existing UDF learning approaches.
Evaluations on the DeepFashion3D, DTU, and BlendedMVS datasets validate the
robustness and effectiveness of our proposed approach. In both quantitative
metrics and visual quality, the results indicate our superior performance over
other UDF learning techniques in reconstructing 3D non-watertight models from
multi-view images. Our code is available at
https://bitbucket.org/jkdeng/2sudf/.
