Adaptive Progressive Photon Mapping

Anton S. Kaplanyan and Carsten Dachsbacher

ACM Transactions on Graphics (presented at SIGGRAPH 2013)


My latest publication on difficult paths is also available here.



This paper introduces a novel locally-adaptive progressive photon mapping technique which optimally balances noise and bias in rendered images to minimize the overall error. It is the result of an analysis of the radiance estimation in progressive photon mapping. As a first step, we establish a connection to the field of recursive estimation and regression in statistics and derive the optimal estimation parameters for the asymptotic convergence of existing approaches. Next, we show how to reformulate photon mapping as a spatial regression in the measurement equation of light transport. This reformulation allows us to derive a novel data-driven bandwidth selection technique for estimating a pixel's measurement. The proposed technique possesses attractive convergence properties with finite numbers of samples, which is important for progressive rendering, and it also provides better results for quasi-converged images. Our results show the practical benefits of using our adaptive method.
Paper (with thumbnails)
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author = {Kaplanyan, Anton S. and Dachsbacher, Carsten},
title = {Adaptive Progressive Photon Mapping},
journal = {ACM Transactions on Graphics},
volume = {32},
number = {2},
month = apr,
year = {2013},
pages = {16:1-16:13}
publisher = {ACM},
address = {New York, NY, USA},
keywords = {global illumination, photon mapping, density estimation}

The same set of photons was used for both pairs of the progressive comparison (top pair of images) and the converged comparison (bottom pair). The images in the top part are after 5 million photons shot (about 15 seconds). The bottom pair is a result after 100 billion photon (about 4 hours).

PPM 5 mln photons APPM 5 mln photons PPM final APPM final
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