Fast collision detection between complex solids using rasterizing graphics hardware

We present an efficient method for detecting collisions between complex solid objects. The method features a stable processing time and low sensitivity to the complexity of contact between objects. The algorithm handles both concave and convex objects; however, the best performance is achieved when at least one object is convex in the proximity of the collision zone (our techniques check the required convexity property as a byproduct of the calculations). The method achieves real-time performance when calculations are supported by the standard functionality of graphics hardware available on high-end workstations.     

基于质点的可变形体自碰撞检测

自碰撞检测是可变形体模拟过程中最耗时的环节,提出一种使用图形硬件的快速算法。算法以质点而非三角形作为自碰撞检测的基本单元,用球体包围以质点为中心的局部区域,再用AABB包围该球体的运动轨迹并将数据组织成纹理送入GPU,通过两遍离屏渲染计算出碰撞对集合及每个碰撞对的碰撞发生时间,算法复杂度为O(n)。实验结果表明,使用该算法在大规模布料模拟中检测自碰撞,效率较高。     

Fast computation of spatial selections and joins using graphics hardware

Spatial database operations are typically performed in two steps. In the filtering step, indexes and the minimum bounding rectangles (MBRs) of the objects are used to quickly determine a set of candidate objects. In the refinement step, the actual geometries of the objects are retrieved and compared to the query geometry or each other. Because of the complexity of the computational geometry algorithms involved, the CPU cost of the refinement step is usually the dominant cost of the operation for complex geometries such as polygons. Although many run-time and pre-processing-based heuristics have been proposed to alleviate this problem, the CPU cost still remains the bottleneck. In this paper, we propose a novel approach to address this problem using the efficient rendering and searching capabilities of modern graphics hardware. This approach does not require expensive pre-processing of the data or changes to existing storage and index structures, and is applicable to both intersection and distance predicates. We evaluate this approach by comparing the performance with leading software solutions. The results show that by combining hardware and software methods, the overall computational cost can be reduced substantially for both spatial selections and joins. We integrated this hardware/software co-processing technique into a popular database to evaluate its performance in the presence of indexes, pre-processing and other proprietary optimizations. Extensive experimentation with real-world data sets show that the hardware-accelerated technique not only outperforms the run-time software solutions but also performs as well if not better than pre-processing-assisted techniques.     

Cache-efficient numerical algorithms using graphics hardware

We present cache-efficient algorithms for scientific computations using graphics processing units (GPUs). Our approach is based on mapping the nested loops in the numerical algorithms to the texture mapping hardware and efficiently utilizing GPU caches. This mapping exploits the inherent parallelism, pipelining and high memory bandwidth on GPUs. We further improve the performance of numerical algorithms by accounting for the same relative memory address accesses performed at data elements in nested loops. Based on the similarity of memory accesses performed at the data elements in the input array, we decompose the input arrays into sub-arrays with similar memory access patterns and execute on the sub-arrays for faster execution. Our approach achieves high memory performance on GPUs by tiling the computation and thereby improving the cache-efficiency. Overall, our formulation for GPU-based algorithms extends the current graphics runtime APIs without exposing the underlying hardware complexity to the programmer. This makes it possible to achieve portability and higher performance across different GPUs. We use this approach to improve the performance of GPU-based sorting, fast Fourier transform and dense matrix multiplication algorithms. We also compare our results with prior GPU-based and CPU-based implementations on high-end processors. In practice, we observe 2–10× improvement in performance.     

Accessibility analysis using computer graphics hardware

Analyzing the accessibility of an object's surface to probes or tools is important for many planning and programming tasks that involve spatial reasoning and arise in robotics and automation. The paper presents novel and efficient algorithms for computing accessible directions for tactile probes used in 3D digitization with Coordinate Measuring Machines. The algorithms are executed in standard computer graphics hardware. They are a nonobvious application of rendering hardware to scientific and technological areas beyond computer graphics     

Simulation of stochastic processes using graphics hardware

Graphics Processing Units (GPUs) were originally designed to manipulate images, but due to their intrinsic parallel nature, they turned into a powerful tool for scientific applications. In this article, we evaluated GPU performance in an implementation of a traditional stochastic simulation – the correlated Brownian motion. This movement can be described by the Generalized Langevin Equation (GLE), which is a stochastic integro-differential equation, with applications in many areas like anomalous diffusion, transport in porous media, noise analysis, quantum dynamics, among many others. Our results show the power inherent in GPU programming when compared to traditional CPUs (Intel): we observed acceleration values up to sixty times by using a NVIDIA GPU in place of a single-core Intel CPU.     

Computer graphics hardware

The author predicts that computer graphics will move toward intellect support through better visualization tools based on a 30% per year improvement in hardware. He discusses graphics languages, visualization, processors, workstations, application-specific integrated circuits graphics (ASICs), memory and image files, and medical uses     

Finding feasible mold parting directions using graphics hardware

We present new programmable graphics hardware accelerated algorithms to test the 2-moldability of geometric parts and assist with part redesign. These algorithms efficiently identify and graphically display undercuts as well as minimum and insufficient draft angles. Their running times grow only linearly with respect to the number of facets in the solid model, making them efficient subroutines for our algorithms that test whether a tessellated CAD model can be manufactured in a two-part mold. We have developed and implemented two such algorithms to choose candidate directions to test for 2-moldability using accessibility analysis and Gauss maps. The efficiency of these algorithms lies in the fact that they identify groups of candidate directions such that if any one direction in the group is undercut-free, all are, or if any one is not undercut-free, none are. We examine trade-offs between the algorithms' speed, accuracy, and whether they guarantee that an undercut-free direction will be found for a part if one exists.     

Feature tracking and matching in video using programmable graphics hardware

This paper describes novel implementations of the KLT feature tracking and SIFT feature extraction algorithms that run on the graphics processing unit (GPU) and is suitable for video analysis in real-time vision systems. While significant acceleration over standard CPU implementations is obtained by exploiting parallelism provided by modern programmable graphics hardware, the CPU is freed up to run other computations in parallel. Our GPU-based KLT implementation tracks about a thousand features in real-time at 30 Hz on 1,024 × 768 resolution video which is a 20 times improvement over the CPU. The GPU-based SIFT implementation extracts about 800 features from 640 × 480 video at 10 Hz which is approximately 10 times faster than an optimized CPU implementation.     

View-dependent refinement of multiresolution meshes using programmable graphics hardware

View-dependent multiresolution rendering places a heavy load on CPU. This paper presents a new method on view-dependent refinement of multiresolution meshes by using the computation power of modern programmable graphics hardware (GPU). Two rendering passes using this method are included. During the first pass, the level of detail selection is performed in the fragment shaders. The resultant buffer from the first pass is taken as the input texture to the second rendering pass by vertex texturing, and then the node culling and triangulation can be performed in the vertex shaders. Our approach can generate adaptive meshes in real-time, and can be fully implemented on GPU. The method improves the efficiency of mesh simplification, and significantly alleviates the computing load on CPU.     

Adjacency-based culling for continuous collision detection

We present an efficient approach to reduce the number of elementary tests for continuous collision detection between rigid and deformable models. Our algorithm exploits connectivity information and uses the adjacency relationships between triangles to perform hierarchical culling. This can be combined with table-based lookups to eliminate duplicate elementary tests. In practice, our approach can reduce the number of elementary tests by two orders of magnitude. We demonstrate the performance of our algorithm on various challenging rigid body and deformable simulations.     

三维图形系统中的物体裁剪问题

三维图形系统是三维游戏引擎中的子引擎部分,负责处理三维世界的数据结构以及从玩家或相机所在的视点渲染三维世界。其中的一个重要问题就是物体剪裁问题。对物体裁剪的工作原理进行了详细的分析,提出了一个新的简便的裁剪算法,并给出了该算法的关键代码。     

Real-time virtual environment signal extraction and denoising using programmable graphics hardware

The sense of being within a three-dimensional (3D) space and interacting with virtual 3D objects in a computer-generated virtual environment (VE) often requires essential image, vision and sensor signal processing techniques such as differentiating and denoising. This paper describes novel implementations of the Gaussian filtering for characteristic signal extraction and wavelet-based image denoising algorithms that run on the graphics processing unit (GPU). While significant acceleration over standard CPU implementations is obtained through exploiting data parallelism provided by the modern programmable graphics hardware, the CPU can be freed up to run other computations more efficiently such as artificial intelligence (AI) and physics. The proposed GPU-based Gaussian filtering can extract surface information from a real object and provide its material features for rendering and illumination. The wavelet-based signal denoising for large size digital images realized in this project provided better realism for VE visualization without sacrificing real-time and interactive performances of an application.     

Computation on programmable graphics hardware

GPUs have evolved into powerful and flexible streaming processors with fully programmable floating-point pipelines and tremendous aggregate computational power and memory bandwidth. With these advances, modern GPUs can now perform more functions than the specific graphics computations for which they were designed. This article describes approaches to using GPU processing power to accelerate traditionally CPU-based tasks. We discuss some important characteristics of algorithms that make them good candidates for GPU acceleration. We discuss a specific GPU image-processing application that is a common postprocess for many physically based rendering systems.     

Fast collision detection using the A-buffer

This paper presents a novel and fast image-space collision detection algorithm with the A-buffer, where the GPU computes the potentially colliding sets (PCSs), and the CPU performs the standard triangle intersection test. When the bounding boxes of two objects intersect, the intersection is passed to the GPU. The object surfaces in the intersection are rendered into the A-buffer. Rendering into the A-buffer is up to eight-times faster than the ordinary approaches. Then, PCSs are computed by comparing the depth values of each texel of the A-buffer. A PCS consists of only two triangles. The PCSs are read back to the CPU, and the CPU computes the intersection points between the triangles. The proposed algorithm runs extremely fast, does not require any preprocessing, can handle dynamic objects including deformable and fracturing models, and can compute self-collisions. Such versatility and performance gain of the proposed algorithm prove its usefulness in real-time applications such as 3D games.     

Selection criteria for graphics hardware

Engineering design and drafting applications currently account for the largest number of computer graphics applications; however, experts predict that the growth of business graphics applications will exceed all others in the industry. Selection criteria for the various graphics hardware components commonly used in business environments—displays, terminals, hardcopy devices, input devices and storage media—will be discussed, with an emphasis on technology trends.     

Volume-preserving FFD for programmable graphics hardware

Free-Form Deformation (FFD) is a well established technique for deforming arbitrary object shapes in space. Although more recent deformation techniques have been introduced, among them skeleton-based deformation and cage-based deformation, the simple and versatile nature of FFD is a strong advantage, and justifies its presence in nowadays leading commercial geometric modeling and animation software systems. Since its introduction in the late 1980s, many improvements have been proposed to the FFD paradigm, including control lattices of arbitrary topology, direct shape manipulation and GPU implementation. Several authors have addressed the problem of volume-preserving FFD. These previous approaches either make use of expensive nonlinear optimization techniques, or resort to first order approximation suitable only for small-scale deformations. In this paper we take advantage of the multi-linear nature of the volume constraint in order to derive a simple, exact and explicit solution to the problem of volume-preserving FFD. Two variants of the algorithm are given, without and with direct shape manipulation. Moreover, the linearity of our solution enables to implement it efficiently on GPU.     

基于图形硬件的双调排序算法优化

介绍一种新的并行排序算法,该算法以双调归并排序为基础,运用图形硬件的并行体系结构和二叉排序树数据结构的优点,用部分并行代替所有阶段的顺序执行,对双调排序算法进行优化.对该算法进行分析,在理论上n个序列在P个流处理器上的排序,最优的时间复杂度为O((nlogn)/p).实验测试结果表明,优化后的算法比其它基于图形硬件的双调归并排序算法所用时间短.     

Fast and simple hardware accelerated voxelizations using simplicial coverings

Voxelization of solids, that is the representation of a solid by a set of voxels that approximates it, is an operation with important applications in fields like solid modeling, physical simulation or volume graphics. Moreover, the new generation of affordable 3D raster displays has renewed the interest on fast voxelization algorithms, as the scan-conversion of a solid is a basic operation on these devices. In this paper a hardware accelerated method for computing a voxelization of a polyhedron is presented. The algorithm is simple, efficient, robust and handles any kind of polyhedron (self-intersecting, with or without holes, manifold or non-manifold). Three different implementations are described in detail. The first is a conventional implementation in the CPU, the second is a hardware accelerated implementation that uses standard OpenGL primitives, and the third exploits the capabilities of modern GPUs by using vertex programs.     

Fast inverse offset computation using polygon rendering hardware

Mold and die parts are usually fabricated using 3-axis numerically controlled milling machines with ball-end, flat-end or round-end cutters. The cutter location (CL) surface representing a trajectory surface of the cutter's reference point when the cutter is slid over a part is important for preventing the gouging problem. This surface is equivalent to the inverse offset shape of the part, which is the top surface of the swept volume of the inverse cutter moving around the part surface. The author proposes a fast computation method of the inverse offset shape of a polyhedral part using the hidden-surface elimination mechanism of the polygon rendering hardware. In this method, the CL surface is obtained by simply rendering the component objects of the swept volume. An experimental program is implemented and demonstrated.