We present a novel method, called NeTO, for capturing 3D geometry of solid transparent objects from 2D images via volume rendering. Reconstructing transparent objects is a very challenging task, which is ill-suited for general-purpose reconstruction techniques due to the specular light transport phenomena. Although existing refraction-tracing based methods, designed specially for this task, achieve impressive results, they still suffer from unstable optimization and loss of fine details, since the explicit surface representation they adopted is difficult to be optimized, and the self-occlusion problem is ignored for refraction-tracing. In this paper, we propose to leverage implicit Signed Distance Function (SDF) as surface representation, and optimize the SDF field via volume rendering with a self-occlusion aware refractive ray tracing. The implicit representation enables our method to be capable of reconstructing high-quality reconstruction even with a limited set of images, and the self-occlusion aware strategy makes it possible for our method to accurately reconstruct the self-occluded regions. Experiments show that our method achieves faithful reconstruction results and outperforms prior works by a large margin. Visit our project page at \url{https://www.xxlong.site/NeTO/}

翻译：我们提出一种名为NeTO的新方法，通过体渲染从二维图像中捕获固态透明物体的三维几何形状。由于镜面光传输现象的存在，透明物体重建是一项极具挑战性的任务，通用重建技术难以适用。尽管现有为此任务专门设计的基于折射追踪的方法取得了显著成果，但由于其采用的显式表面表示难以优化且忽略了折射追踪中的自遮挡问题，这些方法仍面临优化不稳定与细节丢失的困境。本文提出利用隐式符号距离函数（SDF）作为表面表示，并通过结合自遮挡感知折射光线追踪的体渲染技术优化SDF场。隐式表示使我们的方法即便在有限图像集下也能实现高质量重建，而自遮挡感知策略则确保了自遮挡区域的精确重建。实验表明，我们的方法能够获得忠实于真实结构的重建结果，并大幅超越了先前工作。项目页面见 \url{https://www.xxlong.site/NeTO/}