Transient Convolutional Imaging.

While traditional imaging systems directly measure scene properties, computational imaging systems add computation to the measurement process, allowing such systems to extract non-trivially encoded scene features. This work demonstrates that exploiting structure in this process allows to recover information that is conventionally considered to be "lost". Relying on temporally and spatially convolutional structure, we extract a novel image modality that was essentially "invisible" before: a new temporal dimension of light propagation, obtained with consumer depth cameras. Using conventional Time-of-Flight cameras, a few seconds of capture and computation allows us to recover information that before could only be acquired in hours of capture time with specialized instrumentation at orders of magnitude higher cost. The novel type of image we capture allows us to make first steps toward the full inversion of light transport. Specifically, we demonstrate that Non-Line-of-Sight imaging and imaging in scattering media can be made feasible with the temporally resolved light transport acquired using Time-of-Flight depth cameras.