We are just setting up our new biometric lab with 3D/4D cameras from 3dMD, Point Grey Gazelle high speed cameras, Kinect and Canon EOS cameras. The equipment will be primarily used for 3D and 4D reconstruction experiments by Jason Xie and Gary Tam.
Today we took delivery of our new Xeon Phi machine. We have 4 Xeon Phis and 2 Xeon 12 core CPUs making 260 cores in this single PC chassis. Ben Mora will supervise 2 new PhD students working on Physics problems and techniques with this machine. Mark W. Jones will also have a new PhD student that will be able to exploit this machine for ray tracing. Generally the machine will be used throughout the group as a platform for parallel computation using the new Intel MIC architecture.
This equipment was funded by the Welsh Government through RIVIC.
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.
Ian 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.