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

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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.