Yesterday ATI announced its new line of GPUs, the X1K family. This family includes the flagship Radeon X1800 XT and XL GPUs (codenamed R520), the mid-range Radeon X1600 XT and Pro GPUs (code named RV530), and the mainstream Radeon X1300 and X1300 Pro GPUs (code named RV515). For a detailed overview, see the articles at ExtremeTech or Beyond3D. ATI has also announced preliminary plans to enable GPGPU development by publishing a detailed spec and a thin abstraction interface for programming the new GPUs.

This paper presents a fast approximation method to obtain the point hit by a reflection or refraction ray. The calculation is based on the distance values stored in environment map texels. This approximation is used to localize environment mapped reflections and refractions; that is, to make them depend on where they occur. On the other hand, placing the eye into the light source, the method is also good to generate real-time caustics. Computing a map for each refractor surface, we can even evaluate multiple refractions without tracing rays. The method is fast and accurate if the scene consists of larger planar faces, when the results are similar to that of ray-tracing. On the other hand, the method suits very well to the GPU architecture, and can render ray-tracing and global illumination effects at a few hundred frames per second. The primary application area of the proposed method is the introduction of these effects in games. (Approximate Ray-Tracing on the GPU with Distance Impostors. Laszlo Szirmay-Kalos, Barnabas Aszodi, Istvan Lazanyi, and Matyas Premecz. Department of Control Engineering and Information Technology, Technical University of Budapest.)

Robust and accurate foreground-background segmentation is a relatively small but crucial step in several computer vision applications. It is a key element in surveillance, 3D-modelling from silhouettes, motion capture, or gesture analysis for human-computer interaction (HCI). For several of these, real-time processing is of main importance and thus should be extremely fast. This work by Andreas Griesser of ETH Zurich proposes a high-speed GPU-based implementation that processes image sequences in less than 4ms per frame and frees the CPU from this processing step altogether. Resulting segmentation exhibits compactness and smoothness in foreground areas as well as for inter-frame temporal contiguity. (Project homepage and software download, Andreas Griesser, Computer Vision Lab, ETH Zuerich.)