NeUDF: Learning Unsigned Distance Fields from Multi-view Images for Reconstructing Non-watertight Models
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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.
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