“CPPM: chi-squared progressive photon mapping” by Lin, Li, Zeng, Zhang, Jia, et al. …
Conference:
Type(s):
Title:
- CPPM: chi-squared progressive photon mapping
Session/Category Title: Light Transport: Methods
Presenter(s)/Author(s):
Abstract:
We present a novel chi-squared progressive photon mapping algorithm (CPPM) that constructs an estimator by controlling the bandwidth to obtain superior image quality. Our estimator has parametric statistical advantages over prior nonparametric methods. First, we show that when a probability density function of the photon distribution is subject to uniform distribution, the radiance estimation is unbiased under certain assumptions. Next, the local photon distribution is evaluated via a chi-squared test to determine whether the photons follow the hypothesized distribution (uniform distribution) or not. If the statistical test deems that the photons inside the bandwidth are uniformly distributed, bandwidth reduction should be suspended. Finally, we present a pipeline with a bandwidth retention and conditional reduction scheme according to the test results. This pipeline not only accumulates sufficient photons for a reliable chi-squared test, but also guarantees that the estimate converges to the correct solution under our assumptions. We evaluate our method on various benchmarks and observe significant improvement in the running time and rendering quality in terms of mean squared error over prior progressive photon mapping methods.
References:
1. VB Bagdonavicius and MS Nikulin. 2011. Chi-squared goodness-of-fit test for right censored data. International Journal of Applied Mathematics and Statistics™ 24, SI-11A (2011), 30–50.Google Scholar
2. Jiating Chen, Bin Wang, and Jun-Hai Yong. 2011. Improved stochastic progressive photon mapping with metropolis sampling. Computer Graphics Forum 30, 4 (2011), 1205–1213.Google ScholarDigital Library
3. William G. Cochran. 1952. The Chi-square Test of Goodness of Fit. The Annals of Mathematical Statistics 23, 3 (1952), 315–345.Google ScholarCross Ref
4. Philip Dutre, Philippe Bekaert, and Kavita Bala. 2006. Advanced global illumination. A K Peters Ltd.Google Scholar
5. Zhe Fu and Henrik Wann Jensen. 2012. Noise reduction for progressive photon mapping. In ACM SIGGRAPH 2012 Talks. ACM Siggraph, 29.Google ScholarDigital Library
6. Iliyan Georgiev, Jaroslav Křivánek, Tomáš Davidovič, and Philipp Slusallek. 2012. Light transport simulation with vertex connection and merging. ACM Trans. Graph. 31, 6 (2012), 192–1.Google ScholarDigital Library
7. Pascal Grittmann, Arsène Pérard-Gayot, Philipp Slusallek, and Jaroslav Křivánek. 2018. Efficient Caustic Rendering with Lightweight Photon Mapping. Computer Graphics Forum 37, 4 (2018), 133–142.Google ScholarCross Ref
8. Adrien Gruson, Mickaël Ribardière, Martin Šik, Jiří Vorba, Rémi Cozot, Kadi Bouatouch, and Jaroslav Křivánek. 2016. A Spatial Target Function for Metropolis Photon Tracing. ACM Transactions on Graphics (TOG) 36, 1 (2016), 4.Google Scholar
9. Tobias Günther and Thorsten Grosch. 2014. Distributed Out-of-Core Stochastic Progressive Photon Mapping. Computer Graphics Forum 33, 6 (2014), 154–166.Google ScholarDigital Library
10. László Györfi, Michael Kohler, Adam Krzyzak, and Harro Walk. 2006. A distribution-free theory of nonparametric regression. Springer Science & Business Media.Google Scholar
11. Toshiya Hachisuka, Wojciech Jarosz, Iliyan Georgiev, Anton Kaplanyan, Derek Nowrouzezahrai, and Ben Spencer. 2013. State of the art in photon density estimation. In SIGGRAPH Asia 2013 Courses. ACM Siggraph, 15.Google Scholar
12. Toshiya Hachisuka, Wojciech Jarosz, and Henrik Wann Jensen. 2010. A progressive error estimation framework for photon density estimation. ACM Transactions on Graphics (TOG) 29, 6 (2010), 144.Google ScholarDigital Library
13. Toshiya Hachisuka and Henrik Wann Jensen. 2009. Stochastic progressive photon mapping. ACM Transactions on Graphics (TOG) 28, 5 (2009), 141.Google ScholarDigital Library
14. Toshiya Hachisuka and Henrik Wann Jensen. 2011. Robust adaptive photon tracing using photon path visibility. ACM Transactions on Graphics (TOG) 30, 5 (2011), 114.Google ScholarDigital Library
15. Toshiya Hachisuka, Shinji Ogaki, and Henrik Wann Jensen. 2008. Progressive photon mapping. ACM Transactions on Graphics (TOG) 27, 5 (2008), 130.Google ScholarDigital Library
16. Toshiya Hachisuka, Jacopo Pantaleoni, and Henrik Wann Jensen. 2012. A path space extension for robust light transport simulation. ACM Transactions on Graphics (TOG) 31, 6 (2012), 191.Google ScholarDigital Library
17. Vlastimil Havran, Jiří Bittner, Robert Herzog, and Hans-Peter Seidel. 2005. Ray maps for global illumination. In Proceedings of the Sixteenth Eurographics conference on Rendering Techniques. Eurographics Association, 43–54.Google Scholar
18. Rubén Jesus García Hernández, Carlos Urena, Jordi Poch, and Mateu Sbert. 2014. Overestimation and underestimation biases in photon mapping with non-constant kernels. IEEE transactions on visualization and computer graphics 20, 10 (2014), 1441–1450.Google ScholarCross Ref
19. Robert Herzog, Vlastimil Havran, Shinichi Kinuwaki, Karol Myszkowski, and HansPeter Seidel. 2007. Global illumination using photon ray splatting. Computer Graphics Forum 26, 3 (2007), 503–513.Google ScholarCross Ref
20. Heinrich Hey and Werner Purgathofer. 2002. Advanced radiance estimation for photon map global illumination. Computer Graphics Forum 21, 3 (2002), 541–545.Google ScholarCross Ref
21. Wenzel Jakob. 2010. Mitsuba renderer.Google Scholar
22. Wenzel Jakob, Christian Regg, and Wojciech Jarosz. 2011. Progressive Expectation-Maximization for Hierarchical Volumetric Photon Mapping. Computer Graphics Forum (Proceedings of EGSR) 30, 4 (June 2011). https://doi.org/10/dtwcjjGoogle Scholar
23. Adrian Jarabo, Julio Marco, Adolfo Muñoz, Raul Buisan, Wojciech Jarosz, and Diego Gutierrez. 2014. A Framework for Transient Rendering. ACM Transactions on Graphics (SIGGRAPH Asia 2014) 33, 6, Article 177 (2014).Google Scholar
24. Wojciech Jarosz, Derek Nowrouzezahrai, Robert Thomas, Peter-Pike Sloan, and Matthias Zwicker. 2011. Progressive photon beams. ACM Transactions on Graphics (TOG) 30, 6 (2011), 181.Google ScholarDigital Library
25. Henrik Wann Jensen. 1996. Global illumination using photon maps. In Rendering Techniques’ 96. Springer, 21–30.Google Scholar
26. Henrik Wann Jensen. 2001. Realistic image synthesis using photon mapping. Vol. 364. Ak Peters Natick.Google ScholarDigital Library
27. Daniel Jönsson and Anders Ynnerman. 2017. Correlated Photon Mapping for Interactive Global Illumination of Time-Varying Volumetric Data. IEEE Transactions on Visualization and Computer Graphics 23, 1 (2017), 901–910.Google ScholarDigital Library
28. Anton S Kaplanyan and Carsten Dachsbacher. 2013. Adaptive progressive photon mapping. ACM Transactions on Graphics (TOG) 32, 2 (2013), 16.Google ScholarDigital Library
29. Claude Knaus and Matthias Zwicker. 2011. Progressive photon mapping: A probabilistic approach. ACM Transactions on Graphics (TOG) 30, 3 (2011), 25.Google ScholarDigital Library
30. Steven G Parker, James Bigler, Andreas Dietrich, Heiko Friedrich, Jared Hoberock, David Luebke, David McAllister, Morgan McGuire, Keith Morley, Austin Robison, et al. 2010. Optix: a general purpose ray tracing engine. ACM Transactions on Graphics (TOG) 29, 4 (2010), 66.Google ScholarDigital Library
31. Hao Qin, Xin Sun, Qiming Hou, Baining Guo, and Kun Zhou. 2015. Unbiased photon gathering for light transport simulation. ACM Transactions on Graphics (TOG) 34, 6 (2015), 208.Google ScholarDigital Library
32. Lars Schjøth. 2009. Anisotropic density estimation in global illumination: a journey through time and space. Ph.D. Dissertation. Ph. D. thesis, University of Copenhagen.Google Scholar
33. Roland Schregle. 2003. Bias compensation for photon maps. Computer Graphics Forum 22, 4 (2003), 729–742.Google ScholarCross Ref
34. Martin Šik, Hisanari Otsu, Toshiya Hachisuka, and Jaroslav Křivánek. 2016. Robust light transport simulation via metropolised bidirectional estimators. ACM Trans. Graph 35, 6 (2016), 245.Google ScholarDigital Library
35. Ben Spencer and Mark W Jones. 2009a. Hierarchical photon mapping. IEEE Transactions on visualization and computer graphics 15, 1 (2009), 49–61.Google ScholarDigital Library
36. Ben Spencer and Mark W Jones. 2009b. Into the blue: Better caustics through photon relaxation. Computer Graphics Forum 28, 2 (2009), 319–328.Google ScholarCross Ref
37. Ben Spencer and Mark W Jones. 2013a. Photon parameterisation for robust relaxation constraints. Computer Graphics Forum 32, 2pt1 (2013), 83–92.Google Scholar
38. Ben Spencer and Mark W Jones. 2013b. Progressive photon relaxation. ACM Transactions on Graphics (TOG) 32, 1 (2013), 7.Google ScholarDigital Library
39. Michael A Stephens. 1974. EDF statistics for goodness of fit and some comparisons. Journal of the American statistical Association 69, 347 (1974), 730–737.Google ScholarCross Ref
40. Rui Wang, Rui Wang, Kun Zhou, Minghao Pan, and Hujun Bao. 2009. An efficient GPU-based approach for interactive global illumination. ACM Transactions on Graphics (TOG) 28, 3 (2009), 91.Google ScholarDigital Library
41. Maayan Weiss and Thorsten Grosch. 2012. Stochastic progressive photon mapping for dynamic scenes. Computer Graphics Forum 31, 2pt3 (2012), 719–726.Google Scholar
42. Shilin Zhu, Zexiang Xu, Henrik Wann Jensen, Hao Su, and Ravi Ramamoorthi. 2020. Deep Kernel Density Estimation for Photon Mapping. In Computer Graphics Forum, Vol. 39. Wiley Online Library, 35–45.Google Scholar


