Pedro V. Sander

Texture mapping progressive meshes

Given an arbitrary mesh, we present a method to construct a progressive mesh (PM) such that all meshes in the PM sequence share a common texture parametrization. Our method considers two important goals simultaneously. It minimizes texture stretch (small texture distances mapped onto large surface distances) to balance sampling rates over all locations and directions on the surface. It also minimizes texture deviation (“slippage” error based on parametric correspondence) to obtain accurate textured mesh approximations. The method begins by partitioning the mesh into charts using planarity and compactness heuristics. It creates a stretch-minimizing parametrization within each chart, and resizes the charts based on the resulting stretch. Next, it simplifies the mesh while respecting the chart boundaries. The parametrization is re-optimized to reduce both stretch and deviation over the whole PM sequence. Finally, the charts are packed into a texture atlas. We demonstrate using such atlases to sample color and normal maps over several models.

Multi-chart geometry images

We introduce multi-chart geometry images, a new representation for arbitrary surfaces. It is created by resampling a surface onto a regular 2D grid. Whereas the original scheme of Gu et al. maps the entire surface onto a single square, we use an atlas construction to map the surface piecewise onto charts of arbitrary shape. We demonstrate that this added flexibility reduces parametrization distortion and thus provides greater geometric fidelity, particularly for shapes with long extremities, high genus, or disconnected components. Traditional atlas constructions suffer from discontinuous reconstruction across chart boundaries, which in our context create unacceptable surface cracks. Our solution is a novel zippering algorithm that creates a watertight surface. In addition, we present a new atlas chartification scheme based on clustering optimization.

Relational joins on graphics processors

We present a novel design and implementation of relational join algorithms for new-generation graphics processing units (GPUs). The most recent GPU features include support for writing to random memory locations, efficient inter-processor communication, and a programming model for general-purpose computing. Taking advantage of these new features, we design a set of data-parallel primitives such as split and sort, and use these primitives to implement indexed or non-indexed nested-loop, sort-merge and hash joins. Our algorithms utilize the high parallelism as well as the high memory bandwidth of the GPU, and use parallel computation and memory optimizations to effectively reduce memory stalls. We have implemented our algorithms on a PC with an NVIDIA G80 GPU and an Intel quad-core CPU. Our GPU-based join algorithms are able to achieve a performance improvement of 2-7X over their optimized CPU-based counterparts.

Relational query coprocessing on graphics processors

Graphics processors (GPUs) have recently emerged as powerful coprocessors for general purpose computation. Compared with commodity CPUs, GPUs have an order of magnitude higher computation power as well as memory bandwidth. Moreover, new-generation GPUs allow writes to random memory locations, provide efficient interprocessor communication through on-chip local memory, and support a general purpose parallel programming model. Nevertheless, many of the GPU features are specialized for graphics processing, including the massively multithreaded architecture, the Single-Instruction-Multiple-Data processing style, and the execution model of a single application at a time. Additionally, GPUs rely on a bus of limited bandwidth to transfer data to and from the CPU, do not allow dynamic memory allocation from GPU kernels, and have little hardware support for write conflicts. Therefore, a careful design and implementation is required to utilize the GPU for coprocessing database queries. In this article, we present our design, implementation, and evaluation of an in-memory relational query coprocessing system, GDB, on the GPU. Taking advantage of the GPU hardware features, we design a set of highly optimized data-parallel primitives such as split and sort, and use these primitives to implement common relational query processing algorithms. Our algorithms utilize the high parallelism as well as the high memory bandwidth of the GPU, and use parallel computation and memory optimizations to effectively reduce memory stalls. Furthermore, we propose coprocessing techniques that take into account both the computation resources and the GPU-CPU data transfer cost so that each operator in a query can utilize suitable processors—the CPU, the GPU, or both—for an optimized overall performance. We have evaluated our GDB system on a machine with an Intel quad-core CPU and an NVIDIA GeForce 8800 GTX GPU. Our workloads include microbenchmark queries on memory-resident data as well as TPC-H queries that involve complex data types and multiple query operators on data sets larger than the GPU memory. Our results show that our GPU-based algorithms are 2--27x faster than their optimized CPU-based counterparts on in-memory data. Moreover, the performance of our coprocessing scheme is similar to, or better than, both the GPU-only and the CPU-only schemes.

Silhouette clipping

Approximating detailed with coarse, texture-mapped meshes results in polygonal silhouettes. To eliminate this artifact, we introduce silhouette clipping, a framework for efficiently clipping the rendering of coarse geometry to the exact silhouette of the original model. The coarse mesh is obtained using progressive hulls, a novel representation with the nesting property required for proper clipping. We describe an improved technique for constructing texture and normal maps over this coarse mesh. Given a perspective view, silhouettes are efficiently extracted from the original mesh using a precomputed search tree. Within the tree, hierarchical culling is achieved using pairs of anchored cones. The extracted silhouette edges are used to set the hardware stencil buffer and alpha buffer, which in turn clip and antialias the rendered coarse geometry. Results demonstrate that silhouette clipping can produce renderings of similar quality to high-resolution meshes in less rendering time.

Signal-specialized parametrization

To reduce memory requirements for texture mapping a model, we build a surface parametrization specialized to its signal (such as color or normal). Intuitively, we want to allocate more texture samples in regions with greater signal detail. Our approach is to minimize signal approximation error --- the difference between the original surface signal and its reconstruction from the sampled texture. Specifically, our signal-stretch parametrization metric is derived from a Taylor expansion of signal error. For fast evaluation, this metric is pre-integrated over the surface as a metric tensor. We minimize this nonlinear metric using a novel coarse-to-fine hierarchical solver, further accelerated with a fine-to-coarse propagation of the integrated metric tensor. Use of metric tensors permits anisotropic squashing of the parametrization along directions of low signal gradient. Texture area can often be reduced by a factor of 4 for a desired signal accuracy compared to non-specialized parametrizations.

Discontinuity edge overdraw

Reduction of aliasing artifacts along discontinuity edges of a rendered polygon mesh is achieved by overdrawing the edges as antialiased lines. The discontinuity edges are oriented consistently and blended as they approach silhouettes in the mesh to avoid popping at the edge, thereby achieving a temporal smoothness at the silhouettes. This temporal smoothness is balanced with a competing desire to maintain spatial sharpness by utilizing an asymmetric blending technique. To further improve results, the discontinuity edges can be sorted by depth prior to overdrawing them. These processes are effective at reducing the temporal artifact known as “crawling jaggies”.

Fast triangle reordering for vertex locality and reduced overdraw

We present novel algorithms that optimize the order in which triangles are rendered, to improve post-transform vertex cache efficiency as well as for view-independent overdraw reduction. The resulting triangle orders perform on par with previous methods, but are orders magnitude faster to compute. The improvements in processing speed allow us to perform the optimization right after a model is loaded, when more information on the host hardware is available. This allows our vertex cache optimization to often outperform other methods. In fact, our algorithms can even be executed interactively, allowing for re-optimization in case of changes to geometry or topology, which happen often in CAD/CAM applications. We believe that most real-time rendering applications will immediately benefit from these new results.

GPUQP: query co-processing using graphics processors

We present GPUQP, a relational query engine that employs both CPUs and GPUs (Graphics Processing Units) for in-memory query co-processing. GPUs are commodity processors traditionally designed for graphics applications. Recent research has shown that they can accelerate some database operations orders of magnitude over CPUs. So far, there has been little work on how GPUs can be programmed for heavy-duty database constructs, such as tree indexes and joins, and how well a full-fledged GPU query co-processor performs in comparison with their CPU counterparts. In this work, we explore the design decisions in using GPUs for query co-processing using both a graphics API and a general purpose programming model. We then demonstrate the processing flows as well as the performance results of our methods.

Anisotropic blue noise sampling

Blue noise sampling is widely employed for a variety of imaging, geometry, and rendering applications. However, existing research so far has focused mainly on isotropic sampling, and challenges remain for the anisotropic scenario both in sample generation and quality verification. We present anisotropic blue noise sampling to address these issues. On the generation side, we extend dart throwing and relaxation, the two classical methods for isotropic blue noise sampling, for the anisotropic setting, while ensuring both high-quality results and efficient computation. On the verification side, although Fourier spectrum analysis has been one of the most powerful and widely adopted tools, so far it has been applied only to uniform isotropic samples. We introduce approaches based on warping and sphere sampling that allow us to extend Fourier spectrum analysis for adaptive and/or anisotropic samples; thus, we can detect problems in alternative anisotropic sampling techniques that were not yet found via prior verification. We present several applications of our technique, including stippling, visualization, surface texturing, and object distribution.