Volume rendering-based 3D reconstruction from multi-view images has gained popularity in recent years, largely due to the success of neural radiance fields (NeRF). A number of methods have been developed that build upon NeRF and use neural volume rendering to learn signed distance fields (SDFs) for reconstructing 3D models. However, SDF-based methods cannot represent non-watertight models and, therefore, cannot capture open boundaries. This paper proposes a new algorithm for learning an accurate unsigned distance field (UDF) from multi-view images, which is specifically designed for reconstructing non-watertight, textureless models. The proposed method, called NeUDF, addresses the limitations of existing UDF-based methods by introducing a simple and approximately unbiased and occlusion-aware density function. In addition, a smooth and differentiable UDF representation is presented to make the learning process easier and more efficient. Experiments on both texture-rich and textureless models demonstrate the robustness and effectiveness of the proposed approach, making it a promising solution for reconstructing challenging 3D models from multi-view images.

翻译：基于体渲染的多视图图像三维重建近年来日益流行，这主要归功于神经辐射场（NeRF）的成功。目前已发展出多种方法，这些方法以NeRF为基础，利用神经体渲染学习符号距离场（SDF）来重建三维模型。然而，基于SDF的方法无法表示非封闭模型，因此不能捕捉开放边界。本文提出一种新算法，专门用于从多视图图像中学习精确的非符号距离场（UDF），旨在重建非封闭、无纹理模型。该方法名为NeUDF，通过引入一种简单、近似无偏且能感知遮挡的密度函数，解决了现有UDF方法的局限性。此外，本文提出一种平滑且可微的UDF表示，使学习过程更简单高效。在纹理丰富及无纹理模型上的实验表明，该方法具有鲁棒性和有效性，为从多视图图像中重建具有挑战性的三维模型提供了一种有前景的解决方案。