From Transparent to Opaque: Rethinking Neural Implicit Surfaces with $α$-NeuS
This work addresses a specific problem in 3D reconstruction for transparent objects, offering an incremental improvement over existing neural implicit surface methods.
The paper tackles the challenge of reconstructing both transparent and opaque objects from multi-view images, introducing α-NeuS, which extends NeuS to handle materials from fully transparent to fully opaque, achieving practical utility and effectiveness as demonstrated on a new benchmark.
Traditional 3D shape reconstruction techniques from multi-view images, such as structure from motion and multi-view stereo, face challenges in reconstructing transparent objects. Recent advances in neural radiance fields and its variants primarily address opaque or transparent objects, encountering difficulties to reconstruct both transparent and opaque objects simultaneously. This paper introduces $α$-Neus -- an extension of NeuS -- that proves NeuS is unbiased for materials from fully transparent to fully opaque. We find that transparent and opaque surfaces align with the non-negative local minima and the zero iso-surface, respectively, in the learned distance field of NeuS. Traditional iso-surfacing extraction algorithms, such as marching cubes, which rely on fixed iso-values, are ill-suited for such data. We develop a method to extract the transparent and opaque surface simultaneously based on DCUDF. To validate our approach, we construct a benchmark that includes both real-world and synthetic scenes, demonstrating its practical utility and effectiveness. Our data and code are publicly available at https://github.com/728388808/alpha-NeuS.