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 https://www.xxlong.site/NeTO/

翻译：我们提出了一种名为NeTO的新方法，通过体渲染从二维图像中捕获固体透明物体的三维几何结构。由于镜面光传输现象，重建透明物体是一项极具挑战性的任务，通用重建技术难以胜任。尽管现有专门针对该任务的基于折射追踪的方法取得了令人瞩目的成果，但由于其采用的显式表面表示难以优化，且折射追踪忽略了自遮挡问题，这些方法仍面临优化不稳定和细节丢失的困境。在本文中，我们提出利用隐式符号距离函数作为表面表示，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使仅使用有限图像集也能实现高质量重建，而自遮挡感知策略则使方法能够精确重建自遮挡区域。实验表明，我们的方法实现了忠实于真实几何的重建结果，并以显著优势超越了先前工作。访问项目页面：https://www.xxlong.site/NeTO/