  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/
