Lighting Reconstruction Using Fast and Adaptive Density Estimation Techniques
Monte Carlo (MC) photon shooting approach is becoming an important global illumination technique in research and commercial applications. In this work, we focus on the problem of lighting reconstruction for planar surfaces. Our contribution is in the development of new, efficient photon density estimation techniques. We formulate local error measures of lighting reconstruction which under some reasonable constraints (discussed below) imposed on the lighting function that behave like the actual error. The minimization of our error estimates is very fast for planar surfaces and usually leads to a better quality lighting result than traditional methods. Also, the local error estimation offers more information than global error measures usually provided by MC solvers, which are not good predictors of image quality. We compare the actual error resulting from various techniques, and evaluate the visual appearance of the reconstructed lighting.
KeywordsMonte Carlo Near Neighbor Photon Density Global Illumination Variable Kernel
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