Patch Textures: Hardware Support for Mesh Colors.

Mesh Colors provide an effective alternative to standard texture mapping. They significantly simplify the asset production pipeline by removing the need for defining a mapping and eliminate rendering artifacts due to seams. This article addresses the problem that using Mesh Colors for real-time rendering has not been practical, due to the absence of hardware support. We show that it is possible to provide full hardware texture filtering support for Mesh Colors with minimal changes to existing GPUs by introducing a hardware-friendly representation for Mesh Colors that we call Patch Textures, which can have quadrilateral or triangular topology. We discuss the hardware modifications needed for storing and filtering Patch Textures, including anisotropic filtering. This article extends our previous work by discussing and comparing patch edge-handling approaches, including an option for sampling the textures of neighboring patches using an adjacency map. We also provide extensive discussions regarding data duplication, a partial implementation present in existing hardware, and the difficulties with providing a similar hardware support for Ptex.

Mapping high-fidelity volume rendering for medical imaging to CPU, GPU and many-core architectures.

Medical volumetric imaging requires high fidelity, high performance rendering algorithms. We motivate and analyze new volumetric rendering algorithms that are suited to modern parallel processing architectures. First, we describe the three major categories of volume rendering algorithms and confirm through an imaging scientist-guided evaluation that ray-casting is the most acceptable. We describe a thread- and data-parallel implementation of ray-casting that makes it amenable to key architectural trends of three modern commodity parallel architectures: multi-core, GPU, and an upcoming many-core Intel architecture code-named Larrabee. We achieve more than an order of magnitude performance improvement on a number of large 3D medical datasets. We further describe a data compression scheme that significantly reduces data-transfer overhead. This allows our approach to scale well to large numbers of Larrabee cores.