Datensatz

Zusammenfassung

Standard density estimation approaches suffer from visible bias due to low-pass
filtering of the lighting function. Therefore, most photon density estimation
methods have been used primarily with inefficient Monte Carlo final gathering
to achieve high-quality results for the indirect illumination. We present a
density estimation technique for efficiently computing all-frequency global
illumination in diffuse and moderately glossy scenes. In particular, we compute
the direct, indirect, and caustics illumination during photon tracing from the
light sources. Since the high frequencies in the illumination often arise from
visibility changes and surface normal variations, we consider a kernel that
takes these factors into account. To efficiently detect visibility changes, we
introduce a hierarchical voxel data structure of the scene geometry, which is
generated on GPU. Further, we preserve the surface orientation by computing the
density estimation in ray space.