统一重建透明与不透明物体，突破传统隐式表面方法局限
From Transparent to Opaque: Rethinking Neural Implicit Surfaces with $α$-NeuS
- 基于距离场中的非负极小值与零等值面区分透明与不透明表面
- 提出DCUDF方法实现透明与不透明表面同步提取
- 构建真实与合成场景混合基准，验证方法实用性
从多视角图像重建3D形状的传统技术（如结构光与多视图立体）在处理透明物体时存在困难。近期神经辐射场及其变体虽能分别处理不透明或透明物体，但难以同时重建两者。本文提出α-NeuS——对NeuS的扩展，证明NeuS对从完全透明到完全不透明的材质具有无偏性。研究发现，透明表面对应于NeuS学习的距离场中非负局部极小值，而不透明表面则对应于零等值面。传统等值面提取算法（如marching cubes）依赖固定等值面，不适合此类数据。为此，我们基于DCUDF开发了可同时提取透明与不透明表面的方法。为验证该方法，我们构建了一个包含真实世界与合成场景的基准数据集，展示其实际应用价值。数据与代码已公开于https://github.com/728388808/alpha-NeuS。
原文摘要 · Abstract (English)
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.
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