Parallel Light Fields: A Perspective and A Framework

Light fields give relatively complete description of scenes from perspective of angles and positions of rays. At present time, most of the computer vision algorithms take 2D images as input which are simplified expression of light fields with depth information discarded. In theory, computer vision tasks may achieve better performance as long as complete light fields are acquired. Light field data enjoy a natural advantage over images or videos in 3D reconstruction, and are in great demand in applications such as virtual reality (VR) and augmented reality (AR). However, the high cost hardware and complicated synchronization issues in their array deployment severely hindered development of light field cameras. As a consequence, available light field data are much smaller than traditional images and videos in volume, and a lot of deep learning based computer vision algorithms that take light field images as input are difficult to be deployed.