Abstract
We present an adaptive extension of probe based global illumination solution that enhances the response to dynamic changes in the scene while while also enabling an order of magnitude increase in probe count. Our adaptive sampling strategy carefully places samples in regions where we detect time varying changes in radiosity either due to a change in lighting, geometry or both. Even with large number of probes, our technique robustly updates the irradiance and visibility cache to reflect the most up to date changes without stalling the overall algorithm. Our bandwidth aware approach is largely an improvement over the original Dynamic Diffuse Global Illumination while also remaining orthogonal to the recent advancements in the technique.
Comparision
Moving Buddha over glossy surface under ambient lighting. The reflections on the glossy floor from the moving object is missing in Q-DDGI.
Video results
Downloads
Paper: adgi.pdf (2.7MB)
HPG Poster: adgi_poster.pdf (1MB)
Video: gDrive (MP4, 586MB)
Bibtex: adgi.bib
Acknowledgements
We thank Jing Huang for her valuable feedback and helping us with the literature review. We also thank the reviewers for their constructive feedback, the ORCA for the Amazon Lumberyard Bistro model, the Stanford CG Lab for the Buddha model, and Morgan McGuire for the Crytek Sponza models. This work was supported by Huawei Vancouver CG labs and a Ph.D. scholarship from the Fonds de recherche du Québec – nature et technologies.