Seeing around corners, also known as non-line-of-sight (NLOS) imaging is a
computational method to resolve or recover objects hidden around corners.
Recent advances in imaging around corners have gained significant interest.
This paper reviews different types of existing NLOS imaging techniques and
discusses the challenges that need to be addressed, especially for their
applications outside of a constrained laboratory environment. Our goal is to
introduce this topic to broader research communities as well as provide
insights that would lead to further developments in this research area.

本文回顾了不同类型的现有非直线视线成像技术，讨论了需要解决的挑战，特别是在受限实验环境之外的应用。我们的目标是向更广泛的研究社区介绍该主题，并提供进一步发展该研究领域的见解。

绕过拐角成像的最新进展

Recent Advances in Imaging Around Corners

This paper proposes a 3D shape descriptor network, which is a deep
convolutional energy-based model, for modeling volumetric shape patterns. The
maximum likelihood training of the model follows an "analysis by synthesis"
scheme and can be interpreted as a mode seeking and mode shifting process. The
model can synthesize 3D shape patterns by sampling from the probability
distribution via MCMC such as Langevin dynamics. The model can be used to train
a 3D generator network via MCMC teaching. The conditional version of the 3D
shape descriptor net can be used for 3D object recovery and 3D object
super-resolution. Experiments demonstrate that the proposed model can generate
realistic 3D shape patterns and can be useful for 3D shape analysis.

本文提出了一种 3D 形状描述符网络，是一种深度卷积能量模型，可以用于建模体积形状模式，并可以通过 MCMC 模拟实现生成真实的 3D 形状模式并用于 3D 形状分析和恢复。