CGV: Small: Inverse Light Transport Under Femto-Photography and Transient Imaging

CGV：小：Femto下的逆光传输-摄影和瞬变成像Raskar，Ramesh，麻省理工学院如何拍摄视线以外的物体？如何从单一视点恢复材质的双向反射比？这些看似不可能完成的任务可以通过考虑有限的光速和使用一种新型的被称为毫微微摄影的计算摄影技术来实现。超快成像的新进展为计算机图形学和计算机视觉中的光传输建模、表示和合成提供了巨大的新机会。通过分析场景对极短持续时间主动光照的瞬时响应，将有助于计算摄影和场景理解方面的研究。传统成像使用全局照明已达到平衡状态的稳态响应。研究人员正在开发一种新的瞬变光传输理论框架，并使用时间分辨成像解决反问题。研究人员最近开发了第一个隐藏几何恢复的物理演示。这项研究旨在通过开发一个利用时间分辨成像来研究更高维光传输的数学框架，来开发计算成像的一个新分支。这项研究在计算机图形学/视觉和计算摄影领域带来了超快成像。精细采样的时间维度为建模和测量场景的几何和光度学提供了一系列研究方向，这些场景以前被认为超出了传统机器视觉的范围。时间分辨成像技术以新颖的方式利用了多路复用、利用稀疏性的重建、状态空间公式、系统辨识方法和参数化反射模型。总体而言，这项研究将基于光传输的方法的界限推向了一个额外的(时间)维度，并希望表明5D光传输中的正问题和反问题可以启发下一代成像硬件和算法。

CGV: Small: Inverse Light Transport under Femto-Photography and Transient ImagingRaskar, Ramesh, Massachusetts Institute of TechnologyHow can you photograph objects beyond the line of sight? How can you recover bidirectional reflectance of materials from a single viewpoint? These seemingly impossible tasks are possible by considering the finite speed of light and using a new type of computational photography called, Femto-Photography. New advances in ultra-fast imaging provide tremendous new opportunities in modeling, representing and synthesizing light transport in computer graphics and computer vision. Research in computational photography and scene understanding will benefit by analyzing the transient response of the scene to extremely short duration active illumination. Traditional imaging uses steady-state response where the global illumination has reached an equilibrium state. The investigators are developing a new theoretical framework for transient light transport and are addressing inverse problems using time-resolved imaging. The investigators have recently developed the first physical demonstration of hidden geometry recovery.The research aims to develop a new branch of computational imaging by developing a mathematical framework for studying higher dimensional light transport that exploits time-resolved imaging. This research brings ultra-fast imaging in the realm of computer graphics/vision and computational photography. The finely sampled time-dimension provides a range of research directions for modeling and measuring geometry and photometry of scenes that were previously considered beyond the reach of traditional machine vision. The techniques for time-resolved imaging exploit multiplexing, sparsity-exploiting reconstructions, state-space formulation, system identification methods and parameterized reflectance models in novel ways. Overall, the research pushes the boundaries of light transport based methods by an extra (time) dimension and hopes to show that forward and inverse problems in 5D light transport can inspire the next generation of imaging hardware and algorithms.