# A practical light transport system model for chemiluminescence distribution reconstruction

## TL;DR

This paper introduces a flexible light transport model for camera systems, enabling efficient 3D chemiluminescence distribution reconstruction from images, leveraging GPU acceleration for improved performance.

## Contribution

The paper presents a novel light transport-based camera model that is adaptable to various camera types and suitable for GPU implementation, enhancing 3D reconstruction capabilities.

## Key findings

- Successful GPU implementation of the model
- Effective reconstruction of chemiluminescence distributions
- Applicable to both traditional and plenoptic cameras

## Abstract

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.