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Supplementary material for: Combining Global and Local...
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Supplementary material for: Combining Global and Local Virtual Lights for Detailed Glossy Illumination Tom´ aˇ s Davidoviˇ c * Saarland University and DFKI Jaroslav Kˇ riv´ anek Charles University, Prague Cornell University Miloˇ s Haˇ san Harvard University Philipp Slusallek Saarland University and DFKI Kavita Bala Cornell University 1 Stochastic Progressive Photon Mapping In Figures 1 and 2 we provide comparison of our method with CPU implementation of Progressive Photon Mapping [Hachisuka et al. 2008] and Stochastic Progressive Photon Mapping [Hachisuka and Jensen 2009]. The timings are machine with Intel Xeon X5560 processors (with 4 physical, 8 logical cores each). Both algorithms are implemented as CPU only. To compensated for this, we the reported results are for roughly 5× longer runtime for each method, when compared to ours. We let the algorithms run even after this and confirmed that no significant improvement could be achieved by providing slightly longer time. References HACHISUKA, T., AND J ENSEN, H. W. 2009. Stochastic progressive photon mapping. ACM Trans. Graph. 28, 5, 1–8. HACHISUKA, T., OGAKI , S., AND J ENSEN, H. W. 2008. Progressive photon mapping. ACM Trans. Graph. 27, 5, 130:1–130:8. * e-mail: [email protected], [email protected], [email protected], [email protected], [email protected] Our approach Progressive Photon Mapping (PPM) Stochastic PPM 5 min 28 sec (10k maps, 300k glob. lights, 17.1M loc. lights) 26 min 40 sec (4525M photons; 16 cores) 27 min 49 sec (852.5M photons; 16 cores) 5 min 43 sec (5k maps, 200k glob. lights, 55.6M loc. lights) 28 min 28 sec (18100M photons; 16 cores) 29 min 32 sec (8840M photons; 16 cores) Figure 1: Results: Comparison of our method with Progressive Photon Mapping and Stochastic Progressive Photon Mapping
Transcript of Supplementary material for: Combining Global and Local...
Supplementary material for:Combining Global and Local Virtual Lights for Detailed Glossy Illumination
Tomas Davidovic∗
Saarland University and DFKI
Jaroslav KrivanekCharles University, Prague
Cornell University
Milos HasanHarvard University
Philipp SlusallekSaarland University and DFKI
Kavita BalaCornell University
1 Stochastic Progressive Photon Mapping
In Figures 1 and 2 we provide comparison of our method with CPU implementation of Progressive Photon Mapping [Hachisuka et al. 2008]and Stochastic Progressive Photon Mapping [Hachisuka and Jensen 2009]. The timings are machine with Intel Xeon X5560 processors(with 4 physical, 8 logical cores each). Both algorithms are implemented as CPU only. To compensated for this, we the reported results arefor roughly 5× longer runtime for each method, when compared to ours. We let the algorithms run even after this and confirmed that nosignificant improvement could be achieved by providing slightly longer time.
ReferencesHACHISUKA, T., AND JENSEN, H. W. 2009. Stochastic progressive photon mapping. ACM Trans. Graph. 28, 5, 1–8.HACHISUKA, T., OGAKI, S., AND JENSEN, H. W. 2008. Progressive photon mapping. ACM Trans. Graph. 27, 5, 130:1–130:8.
∗e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
Our approach Progressive Photon Mapping (PPM) Stochastic PPM
5 min 28 sec (10k maps, 300k glob. lights, 17.1M loc. lights) 26 min 40 sec (4525M photons; 16 cores) 27 min 49 sec (852.5M photons; 16 cores)
5 min 43 sec (5k maps, 200k glob. lights, 55.6M loc. lights) 28 min 28 sec (18100M photons; 16 cores) 29 min 32 sec (8840M photons; 16 cores)
Figure 1: Results: Comparison of our method with Progressive Photon Mapping and Stochastic Progressive Photon Mapping
Our approach Progressive Photon Mapping (PPM) Stochastic PPM
2 min 44 sec (15k maps, 200 glob. lights, 13.5M loc. lights) 13 min 54 sec (6025M photons; 16 cores) 14 min 20 sec (3275M photons; 16 cores)
4 min 16 sec (10k maps, 100k glob. lights, 25.1M loc. lights) 20 min 50 sec (2175M photons; 16 cores) 21 min 9 sec (452.5M photons; 16 cores)
Figure 2: Results: Comparison of our method with Progressive Photon Mapping and Stochastic Progressive Photon Mapping
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