An Improved Monte Carlo Ray Tracing for Large-Scale Rendering in Hadoop
- 10.2991/csss-14.2014.142How to use a DOI?
- Monte Carlo ray tracing; large-scale scene; scheduling grids; Hadoop
To improve the performance of large-scale rendering, it requires not only a good view of data structure, but also less disk and network access, especially for achieving the realistic visual effects. This paper presents an optimization method of global illumination rendering for large datasets. We improved the previous rendering algorithm based on Monte Carlo ray tracing and the scheduling grids, and reduced the remote reads by slightly organizing the original data with considerations of locality and coherence. We implemented the rendering system in a Hadoop cluster of commodity PCs without high-end hardware. The large scene data are processed in splits by MapReduce framework, which increases scalability and reliability. The result shows that our algorithm of scheduling rays for each data split fits with large-scale scene and takes less reads and rendering time than previous works.
- © 2014, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Li Rui AU - Zheng Yue PY - 2014/06 DA - 2014/06 TI - An Improved Monte Carlo Ray Tracing for Large-Scale Rendering in Hadoop BT - Proceedings of the 3rd International Conference on Computer Science and Service System PB - Atlantis Press SP - 609 EP - 613 SN - 1951-6851 UR - https://doi.org/10.2991/csss-14.2014.142 DO - 10.2991/csss-14.2014.142 ID - Rui2014/06 ER -