Interactive Hyper Spectral Image Rendering on GPU

摘要

In this paper, we describe a framework focused on spectral images rendering. The rendering of a such image leads us to three major issues: the computation time, the footprint of the spectral image, and the memory consumption of the algorithm. The computation time can be drastically reduced by the use of GPUs, however, their memory capacity and bandwidth (compared to their compute power) are limited. When the spectral dimension of the image will raise, the straightforward approach of the Path Tracing will lead us to high memory consumption and latency problems. To overcome these problems, we propose the DPEPT (Deferred Path Evaluation Path Tracing) which consists in decoupling the path evaluation from the path generation. This technique reduces the memory latency and consumption of the Path Tracing. It allows us to use an efficient wavelength samples batches parallelization pattern to optimize the path evaluation step and outperforms the straightforward approach when the spectral resolution of the simulated image increases.