Tag Archives: Path Tracing

Probabilistic illumination-aware filtering for Monte Carlo rendering

Path traced, 16 samples using  Probabilistic illumination-aware filtering

Path traced, 16 samples using Probabilistic illumination-aware filtering

Noise removal for Monte Carlo global illumination rendering is a well known problem, and has seen significant attention from image-based filtering methods. However, many state of the art methods breakdown in the presence of high frequency features, complex lighting and materials. In this work we present a probabilistic image based noise removal and irradiance filtering framework that preserves this high frequency detail such as hard shadows and glossy reflections, and imposes no restrictions on the characteristics of the light transport or materials. We maintain per-pixel clusters of the path traced samples and, using statistics from these clusters, derive an illumination aware filtering scheme based on the discrete Poisson probability distribution. Furthermore, we filter the incident radiance of the samples, allowing us to preserve and filter across high frequency and complex textures without limiting the effectiveness of the filter.

pdficon_largePowerpoint iconIan C. Doidge and Mark W. Jones.
CGI 2013, The Visual Computer 29(6-8),707-616, 2013. The final publication is available at www.springerlink.com.

Mixing Monte Carlo and Progressive Rendering for Improved Global Illumination

In this paper we seek to eliminate the noise caused by caustic paths during progressive Monte Carlo path tracing. We employ a filtering strategy over path space, handling each subspace using specialised derivations of path tracing and progressive photon mapping. Evaluating diffuse paths with path tracing allows the use of sample strati cation over both pixels and the image as a whole, whilst sharp detailed caustics are produced using progressive photon mapping. This is an efficient, low noise progressive algorithm with vanishing bias combining the advantages of both Monte Carlo methods, and particle tracing.

Powerpoint iconIan C. Doidge, Mark W. Jones and Ben Mora.
CGI 2012, The Visual Computer 28(6-8), 603-612, 2012: The final publication is available at www.springerlink.com. [doi] [BibTeX]