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

CGV：小号：飞秒摄影和瞬态成像下的逆光传输马萨诸塞州理工学院拉梅什·拉斯卡如何拍摄视线之外的物体？如何从单个视点恢复材料的双向反射？这些看似不可能的任务是可能的，通过考虑有限的光速和使用一种新型的计算摄影称为，飞秒摄影。超快成像的新进展为计算机图形学和计算机视觉中的建模、表示和合成光传输提供了巨大的新机会。研究计算摄影和场景理解将受益于分析的瞬态响应的场景极短的持续时间主动照明。传统的成像使用稳态响应，其中全局照明已经达到平衡状态。研究人员正在开发一种新的瞬态光传输理论框架，并使用时间分辨成像解决逆问题。研究人员最近开发了第一个隐藏几何恢复的物理演示。该研究旨在通过开发利用时间分辨成像研究高维光传输的数学框架，开发计算成像的新分支。这项研究在计算机图形/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几何和测光场景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。时间分辨成像技术利用多路复用，稀疏性开发重建，状态空间制定，系统识别方法和参数化的反射率模型，以新的方式。总的来说，这项研究通过额外的（时间）维度推动了基于光传输的方法的边界，并希望表明5D光传输中的正问题和逆问题可以激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.