Image-Composition Architectures for Real-Time Image Generation

Abstract

This dissertation describes a new approach for high-speed image-generation based on image compositing. Application software distributes the primitives of a graphics database over a homogeneous array of processors called renderers. Each renderer transforms and rasterizes its primitives to form a full-sized image of its portion of the database. A hardware datapath, called an image-composition network, composites the partial images into a single image of the entire scene. Image-composition architectures are linearly scalable to arbitrary performance. This property arises because: (1) renderers compute their subimages independently, and (2) an image-composition network can accommodate an arbitrary number of renderers, with constant bandwidth in each link of the network. Because they are scalable, image-composition architectures promise to achieve much higher performance than existing commercial or experimental systems. They are flexible as well, supporting a variety of primitive types and rendering algorithms. Also, they are efficient, having approximately the same performance/price ratio as the underlying renderers. Antialiasing is a special challenge for image-composition architectures. The compositing method must retain primitive geometry within each pixel to avoid aliasing. Two alternatives are explored in this dissertation: simple z-buffer compositing combined with supersampling, and A-buffer compositing. This dissertation describes the properties of image-composition architectures and presents the design of a prototype z-buffer-based system called PixelFlow. The PixelFlow design, using only proven technology, is expected to render 2.5 million triangles per second and 870 thousand antialiased triangles per second in a two-card-cage system. Additional card cages can be added to achieve nearly linear increases in performance.