Reconstructing transparent objects from a set of multi-view images is a challenging task due to the complicated nature and indeterminate behavior of light propagation. Typical methods are primarily tailored to specific scenarios, such as objects following a uniform topology, exhibiting ideal transparency and surface specular reflections, or with only surface materials, which substantially constrains their practical applicability in real-world settings. In this work, we propose a differentiable rendering framework for transparent objects, dubbed DiffTrans, which allows for efficient decomposition and reconstruction of the geometry and materials of transparent objects, thereby reconstructing transparent objects accurately in intricate scenes with diverse topology and complex texture. Specifically, we first utilize FlexiCubes with dilation and smoothness regularization as the iso-surface representation to reconstruct an initial geometry efficiently from the multi-view object silhouette. Meanwhile, we employ the environment light radiance field to recover the environment of the scene. Then we devise a recursive differentiable ray tracer to further optimize the geometry, index of refraction and absorption rate simultaneously in a unified and end-to-end manner, leading to high-quality reconstruction of transparent objects in intricate scenes. A prominent advantage of the designed ray tracer is that it can be implemented in CUDA, enabling a significantly reduced computational cost. Extensive experiments on multiple benchmarks demonstrate the superior reconstruction performance of our DiffTrans compared with other methods, especially in intricate scenes involving transparent objects with diverse topology and complex texture. The code is available at https://github.com/lcp29/DiffTrans.

翻译：从一组多视角图像重建透明物体是一项极具挑战性的任务，这源于光传播的复杂性和不确定性。现有方法通常针对特定场景设计，例如物体具有均匀拓扑结构、呈现理想透明度与表面镜面反射，或仅包含表面材质，这严重限制了其在真实场景中的实际适用性。本文提出一种面向透明物体的可微分渲染框架，称为 DiffTrans，该框架能够高效分解并重建透明物体的几何与材质，从而在具有多样拓扑和复杂纹理的复杂场景中精确重建透明物体。具体而言，我们首先采用带膨胀与平滑正则化的 FlexiCubes 作为等值面表示，从多视角物体轮廓中高效重建初始几何。同时，我们利用环境光辐射场恢复场景的环境光照。接着，我们设计了一种递归可微分光线追踪器，以统一且端到端的方式同时优化几何、折射率与吸收率，从而在复杂场景中实现高质量的透明物体重建。所设计光线追踪器的一个突出优势是可在 CUDA 中实现，从而显著降低计算成本。在多个基准数据集上的大量实验表明，我们的 DiffTrans 相比其他方法具有更优的重建性能，尤其是在涉及多样拓扑和复杂纹理的透明物体的复杂场景中。代码公开于 https://github.com/lcp29/DiffTrans。