This research focuses on designing new light transport acquisition frameworks to capture dynamic scenes, characterization of dynamic light transport properties including sparsity and low-rank, and algorithms to exploit this information for computer vision applications. In particular, the project focuses on three main objectives. The first is design of an MEMS-based optical scanner coupled with high frame rate cameras to capture the full set of light transport paths at extremely fast timescales. The second contribution is new algorithms for adaptive light transport sampling using both physics-based and data-driven priors for light transport interpolation via generalized light transport flow. Finally, the project will provide applications of dynamic light transport for 3D scanning of deformable, moving, and specular objects. These innovations are evaluated in an integrated testbed via the optical scanner and the collection of a dataset of dynamic light transport for real-world scenes.