This paper presents the computation of the feature distance transform (FDT) with an arbitrary distance function and the corresponding 2D skeleton or Voronoi diagram in DX8 or DX9 graphics hardware. Together with a direct, non-iterative distance measurement, the FDT enables us to compute a simply connected pixel-exact skeleton, which can be continously pruned with a regularization parameter. Real-time distance minimization in the zooming process delivers even sub-pixel accuracy. There are no restrictions on the distance function and additive and multiplicative weights can be applied. An adaptive tiling scheme delivers a ten-fold performance increase over a software or simple hardware implementation. (Generalized Distance Transforms and Skeletons in Graphics Hardware. Robert Strzodka and Alexandru Telea in Proceedings VisSym’04, to appear, 2004)

This paper presents an implementation of the Generalized Hough Transform (GHT) in DX8 graphics hardware. Given the 3D geometry of an object, the GHT is used to determine its pose, scale and position in an uncalibrated image. Without any a-priori knowledge about the image many different poses and scales must be tested. The implementation achieves a considerable speedup by increasing the operation count in favor of a data stream processing of the otherwise irregular memory access pattern of the GHT. The additional operations are used to regularize the problem, decreasing the number of the required candidate poses. (A Graphics Hardware Implementation of the Generalized Hough Transform for fast Object Recognition, Scale, and 3D Pose Detection. Robert Strzodka, Ivo Ihrke and Marcus Magnor in Proceedings ICIAP 2003, pp. 188-193, 2003.)

This paper proposes algorithms for computing extent measures and approximate representations of a stream of points in R² or R³. In particular, we study the problems of computing various extent measures (for example diameter, width, smallest enclosing rectangle, and smallest enclosing disk) and of approximating a set of points by a circle or a line. We show that these problems can be solved efficiently using graphics hardware even in the streaming model. (P. Agarwal, S. Krishnan, N. Mustafa and S. Venkatasubramanian. Streaming Geometric Optimization Using Graphics Hardware. Proc. 11th European Symposium on Algorithms, Sep 2003.)

This paper from the VIS Group Stuttgart describes the acceleration of so-called morphological operators using graphics hardware and OpenGL. As the problem is mainly memory bandwidth bound, a solution based on graphics hardware can significantly reduce computation time in the filtering step, as graphics hardware typically has much broader and faster memory paths. When using fixed-point graphics hardware for mathematical computations, accuracy can be a problem. However, morphological operators map well onto the graphics pipeline, resulting in no loss of accuracy. See also the project page for more about hardware-based filtering. (Accelerating Morphological Analysis with Graphics Hardware. Matthias Hopf and Thomas Ertl. Workshop on Vision, Modelling, and Visualization 2000. pp 337-345)

Two papers from the VIS Group Stuttgart describe implementations of wavelet-based multi-resolution analysis using OpenGL. Wavelets are commonly used for signal processing and image compression (e.g. for JPEG 2000). The papers focus on details of implementing wavelet decomposition and reconstruction using graphics hardware, and develop a scaled version of wavelet analysis that constrains data to the [0,1] range of fixed-point frame buffers. See also the project page for more about hardware-based filtering. (Hardware-Based Wavelet Transformations. Matthias Hopf and Thomas Ertl. Workshop on Vision, Modeling, and Visualization 1999, pp 317-328. Hardware-Accelerated Wavelet Transformations. Matthias Hopf and Thomas Ertl. Proc. EG/IEEE TCVG Symposium on Visualization VisSym 2000, pp 93-103.)

This paper from the VIS Group Stuttgart shows the first volume filtering algorithm that uses OpenGL for the convolution process. Filtering volume data is useful for noise reduction, feature detection, and segmentation. The process is significantly accelerated on SGI graphics workstations with hardware support for two-dimensional image convolution in the frame buffer. Generic 3D convolution can be added as a powerful tool in interactive volume visualization toolkits. See also the project page for more about hardware-based filtering. (Accelerating 3D Convolution using Graphics Hardware. Matthias Hopf and Thomas Ertl. Proc. Visualization 1999, pp 471–474.)

This dissertation by Mangesh Nijasure of University of Central Florida presents a system for computing plausible global illumination solutions for dynamic environments in real time on programmable graphics processors (GPUs). The dissertation describes a progressive global illumination algorithm to simulate multiple bounces of light on the surfaces of synthetic scenes. The entire algorithm runs on an ATI Radeon 9800 using vertex and fragment shaders, and computes global illumination solutions for reasonably complex scenes with moving objects and moving lights in real time. (Real-time Global Illumination on GPU, Mangesh Nijasure. MS Thesis, Fall 2003)

GPU Gems: Programming Techniques, Tips, and Tricks for Real-Time Graphics, published by Addison-Wesley, is a compilation of articles covering practical real-time graphics techniques. It focuses on the programmable graphics pipeline available in today’s graphics processing units (GPUs) and highlights techniques needed by developers creating advanced visual effects. Several articles in the book deal with GPGPU-related topics. For more information, please visit the book’s web site. GPU Gems will be available at GDC 2004.

This white paper by Batty, Wiebe, and Houston of Frantic Films presents a GPU-based preconditioned conjugate gradient solver used in a production-quality fluid simulator. The paper shows a speedup of 50% over an equivalent CPU-based version, even though it is impossible to implement the optimal preconditioner on the current NVIDIA GPU architecture. (High Performance Production-Quality Fluid Simulation via NVIDIA’s Quadro FX. Chris Batty, Mark Wiebe, and Ben Houston. Frantic Films.)

Brook for GPUs is an active research project at the Stanford University Computer Graphics Lab to explore general-purpose computing on modern programmable graphics hardware. BrookGPU is a compiler and runtime implementation of the Brook stream programming language which provides an easy, C-like programming environment for today’s GPU. The beta version of Brook for GPUs is now available for download at the link below. Brook requires no graphics or GPU programming experience, and supports both ATI Radeon 9500+ and NVIDIA GeForce FX /Quadro FX hardware, using both DirectX and OpenGL APIs. BrookGPU has a complete fallback CPU implementation. (Brook for GPUs.)