Generic Mesh Refinement On GPU

International audienceMany recent publications have shown that a large variety of computation involved in computer graphics can be moved from the CPU to the GPU, by a clever use of vertex or fragment shaders. Nonetheless there is still one kind of algorithms that is hard to translate from CPU to GPU: mesh refinement techniques. The main reason for this, is that vertex shaders available on current graphics hardware do not allow the generation of additional vertices on a mesh stored in graphics hardware. In this paper, we propose a general solution to generate mesh refinement on GPU. The main idea is to define a generic refinement pattern that will be used to virtually create additional inner vertices for a given polygon. These vertices are then translated according to some procedural displacement map defining the underlying geometry (similarly, the normal vectors may be transformed according to some procedural normal map). For illustration purpose, we use a tesselated triangular pattern, but many other refinement patterns may be employed. To show its flexibility, the technique has been applied on a large variety of refinement techniques: procedural displacement mapping, as well as more complex techniques such as curved PN-triangles or ST-meshes

Real-time tessellation of terrain on graphics hardware

Synthetic terrain is a key element in many applications, which can lessen the sense of realism if it is not handled correctly. We propose a new technique for visualizing terrain surfaces by tessellating them on the GPU. The presented algorithm introduces a new adaptive tessellation scheme for managing the level of detail of the terrain mesh, avoiding the appearance of t-vertices that can produce visually disturbing artifacts. Previous solutions exploited the geometry shader's capabilities to tessellate meshes from scratch. In contrast, we reuse the already calculated data to minimize the operations performed in the shader units. This feature allows us to increase performance through smart refining and coarsening. Finally, we also propose a framework to manage large DEMs as height maps.This work has been supported by the Spanish Ministry of Science and Technology (projects TIN2009-14103-C03-03, TSI-020400-2009-0133 and TIN2010-21089-C03-03), by the Generalitat Valenciana (project PROMETEO/2010/028), by Bancaja (project P1 1B2010-08) and by ITEA2 (project IP08009

QAS: Real-time Quadratic Approximation of Subdivision Surfaces.

International audienceWe introduce QAS, an efficient quadratic approximation of subdivision surfaces which offers a very close appearance compared to the true subdivision surface but avoids recursion, providing at least one order of magnitude faster rendering. QAS uses enriched polygons, equipped with edge vertices, and replaces them on-the-fly with low degree polynomials for interpolating positions and normals. By systematically projecting the vertices of the input coarse mesh at their limit position on the subdivision surface, the visual quality of the approximation is good enough for imposing only a single subdivision step, followed by our patch fitting, which allows real-time performances for million polygons output. Additionally, the parametric nature of the approximation offers an efficient adaptive sampling for rendering and displacement mapping. Last, the hexagonal support associated to each coarse triangle is adapted to geometry processors

Implementation of digital pheromones in PSO accelerated by commodity Graphics Hardware

In this paper, a model for Graphics Processing Unit (GPU) implementation of Particle Swarm Optimization (PSO) using digital pheromones to coordinate swarms within ndimensional design spaces is presented. Previous work by the authors demonstrated the capability of digital pheromones within PSO for searching n-dimensional design spaces with improved accuracy, efficiency and reliability in both serial and parallel computing environments using traditional CPUs. Modern GPUs have proven to outperform the number of floating point operations when compared to CPUs through inherent data parallel architecture and higher bandwidth capabilities. The advent of programmable graphics hardware in the recent times further provided a suitable platform for scientific computing particularly in the field of design optimization. However, the data parallel architecture of GPUs requires a specialized formulation for leveraging its computational capabilities. When the objective function computations are appropriately formulated for GPUs, it is theorized that the solution efficiency (speed) can be significantly increased while maintaining solution accuracy. The development of this method together with a number of multi-modal unconstrained test problems are tested and presented in this paper

Rapid Visualization of Large Point-Based Surfaces

International audiencePoint-Based Surfaces can be directly generated by 3D scanners and avoid the generation and storage of an explicit topology for a sampled geometry, which saves time and storage space for very dense and large objects, such as scanned statues and other archaeological artefacts [Duguet 2004]. We propose a fast processing pipeline of large point-based surfaces for real-time, appearance preserving, polygonal rendering. Our goal is to reduce the time needed between a point set made of hundred of millions samples and a high resolution visualization taking benefit of modern graphics hardware, tuned for normal mapping of polygons. Our approach starts by an out-of-core generation of a coarse local triangulation of the original model. The resulting coarse mesh is enriched by applying a set of maps which capture the high frequency features of the original data set. We choose as an example the normal component of samples for these maps, since normal maps provide efficiently an accurate local illumination. But our approach is also suitable for other point attributes such as color or position (displacement map). These maps come also from an out-of-core process, using the complete input data in a streaming process. Sampling issues of the maps are addressed using an efficient diffusion algorithm in 2D. Our main contribution is to directly handle such large unorganized point clouds through this two pass algorithm, without the time-consuming meshing or parameterization step, required by current state-of-the-art high resolution visualization methods. One of the main advantages is to express most of the fine features present in the original large point clouds as textures in the huge texture memory usually provided by graphics devices, using only a lazy local parameterization. Our technique comes as a complementary tool to high-quality, but costly, out-of-core visualization systems. Direct applications are: interactive preview at high screen resolution of very detailed scanned objects such as scanned statues, inclusion of large point clouds in usual polygonal 3D engines and 3D databases browsing

A Flexible Kernel for Adaptive Mesh Refinement on GPU

International audienceWe present a flexible GPU kernel for adaptive on-the-fly refinement of meshes with arbitrary topology. By simply reserving a small amount of GPU memory to store a set of adaptive refinement patterns, on-the-fly refinement is performed by the GPU, without any preprocessing nor additional topology data structure. The level of adaptive refinement can be controlled by specifying a per-vertex depth-tag, in addition to usual position, normal, color and texture coordinates. This depth-tag is used by the kernel to instanciate the correct refinement pattern. Finally, the refined patch produced for each triangle can be displaced by the vertex shader, using any kind of geometric refinement, such as Bezier patch smoothing, scalar valued displacement, procedural geometry synthesis or subdivision surfaces. This refinement engine does neither require multi-pass rendering nor any use of fragment processing nor special preprocess of the input mesh structure. It can be implemented on any GPU with vertex shading capabilities

Rendering Curved Triangles on the GPU

This Thesis presents a new approach to render triangular Bézier patches in real time. The goal is to achieve a very good visual quality, avoid artifacts in the silhouette, and get in nite detail. Our approach consists in a ray casting technique to render tri- angular B ezier patches in real time. It is based on previous work explained in this document to implement a fast ray-surface intersec- tion technique. This previous work consists in adapting Newton's method to implement the intersections achieving interactive framer- ates ray casting di erent surfaces. The main contributions of our approach are adapting New- ton's method to perform intersections with triangular bicubic B ezier patches and implementing it in GPU to optimize performance using graphics hardware. Finally, we also contribute adapting the normal mapping tech- nique to shade the models and, thus, achieve even greater detail

NeuroTessMesh: A Tool for the Generation and Visualization of Neuron Meshes and Adaptive On-the-Fly Refinement

Gaining a better understanding of the human brain continues to be one of the greatest challenges for science, largely because of the overwhelming complexity of the brain and the difficulty of analyzing the features and behavior of dense neural networks. Regarding analysis, 3D visualization has proven to be a useful tool for the evaluation of complex systems. However, the large number of neurons in non-trivial circuits, together with their intricate geometry, makes the visualization of a neuronal scenario an extremely challenging computational problem. Previous work in this area dealt with the generation of 3D polygonal meshes that approximated the cells’ overall anatomy but did not attempt to deal with the extremely high storage and computational cost required to manage a complex scene. This paper presents NeuroTessMesh, a tool specifically designed to cope with many of the problems associated with the visualization of neural circuits that are comprised of large numbers of cells. In addition, this method facilitates the recovery and visualization of the 3D geometry of cells included in databases, such as NeuroMorpho, and provides the tools needed to approximate missing information such as the soma’s morphology. This method takes as its only input the available compact, yet incomplete, morphological tracings of the cells as acquired by neuroscientists. It uses a multiresolution approach that combines an initial, coarse mesh generation with subsequent on-the-fly adaptive mesh refinement stages using tessellation shaders. For the coarse mesh generation, a novel approach, based on the Finite Element Method, allows approximation of the 3D shape of the soma from its incomplete description. Subsequently, the adaptive refinement process performed in the graphic card generates meshes that provide good visual quality geometries at a reasonable computational cost, both in terms of memory and rendering time. All the described techniques have been integrated into NeuroTessMesh, available to the scientific community, to generate, visualize, and save the adaptive resolution meshes

Digital Pheromone Implementation of PSO with Velocity Vector Accelerated by Commodity Graphics Hardware

In this paper, a model for Graphics Processing Unit (GPU) implementation of Particle Swarm Optimization (PSO) using digital pheromones to coordinate swarms within ndimensional design spaces is presented. Particularly, the velocity vector computations are carried out on graphics hardware. Previous work by the authors demonstrated the capability of digital pheromones within PSO for searching n-dimensional design spaces with improved accuracy, efficiency and reliability in serial, parallel and GPU computing environments. The GPU implementation was limited to computing the objective function values alone. Modern GPUs have proven to outperform the number of floating point operations when compared to CPUs through inherent data parallel architecture and higher bandwidth capabilities. This paper presents a method to implement velocity vector computations on a GPU along with objective function evaluations. Three different modes of implementation are studied and presented - First, CPU-CPU where objective function and velocity vector are calculated on CPU alone. Second, GPU-CPU where objective function is computed on the GPU and velocity vector is computed on GPU. Third, GPU-GPU where objective function and velocity vector are both evaluated on the GPU. The results from these three implementations are presented followed by conclusions and recommendations on the best approach for utilizing the full potential of GPUs for PSO