A new approach of density estimation for global illumination

Abstract

This paper presents a new approach to generate view-independent global illumination solution using kernel density estimation. Kernel density estimation allows smooth reconstruction of the radiance from hit points generated by shooting random walk or photon tracing. The advantage of this method is that an unbiased Monte-Carlo algorithm simulates light transport and that light reconstruction introduces error but this error is controllable and purely local. We present an approach that does not require storing the set of hit points generated by photon tracing contrary to previous implementation. A method to reduce error both from the light transport and the light reconstruction is also presented.