What explains Alibaba’s miraculous IPO success on the New York Stock Exchange?

Funding This work was supported by the Natural Science Foundation of China under Grant 72173036; the Natural Science Foundation of Hainan Province under Grant 721RC515. The authors are solely responsible for any error or omission herein. Funding Information: This work was supported by the National Natural Science Foundation of China under Grant 72173036; the Natural Science Foundation of Hainan Province under Grant 721RC515. The authors are solely responsible for any error or omission herein. Publisher Copyright: © 2022 Informa UK Limited, trading as Taylor & Francis Group.Peer reviewe

NeTO:Neural Reconstruction of Transparent Objects with Self-Occlusion Aware Refraction-Tracing

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/