  Plenoptic cameras and other integral photography instruments capture richer
angular information from a scene than traditional 2D cameras. This extra
information is used to estimate depth, perform superresolution or reconstruct
3D information from the scene. Many of these applications involve solving a
large-scale numerical optimization problem. Most published approaches model the
camera(s) using pre-computed matrices that require large amounts of memory and
are not well-suited to modern many-core processors. We propose a flexible
camera model based on light transport and use it to model plenoptic and
traditional cameras. We implement the proposed model on a GPU and use it to
reconstruct simulated and real 3D chemiluminescence distributions (flames) from
images taken by traditional and plenoptic cameras.
