Real-time voxel rendering algorithm based on screen space billboard voxel buffer with sparse lookup textures

In this paper, we present a novel approach to efficient real-time rendering of numerous high-resolution voxelized objects. We present a voxel rendering algorithm based on triangle rasterization pipeline with screen space rendering computational complexity. In order to limit the number of vertex shader invocations, voxel filtering algorithm with fixed size voxel data buffer was developed. Voxelized objects are represented by sparse voxel octree (SVO) structure. Using sparse texture available in modern graphics APIs, we create a 3D lookup table for voxel ids. Voxel filtering algorithm is based on 3D sparse texture ray marching approach. Screen Space Billboard Voxel Buffer is filled by voxels from visible voxels point cloud. Thanks to using 3D sparse textures, we are able to store high-resolution objects in VRAM memory. Moreover, sparse texture mipmaps can be used to control object level of detail (LOD). The geometry of a voxelized object is represented by a collection of points extracted from object SVO. Each point is defined by position, normal vector and texture coordinates. We also show how to take advantage of programmable geometry shaders in order to store voxel objects with extremely low memory requirements and to perform real-time visualization. Moreover, geometry shaders are used to generate billboard quads from the point cloud and to perform fast face culling. As a result, we obtained comparable or even better performance results in comparison to SVO ray tracing approach. The number of rendered voxels is limited to defined Screen Space Billboard Voxel Buffer resolution. Last but not least, thanks to graphics card adapter support, developed algorithm can be easily integrated with any graphics engine using triangle rasterization pipeline

On sparse voxel DAGs and memory efficient compression of surface attributes for real-time scenarios

The general shape of a 3D object can expeditiously be represented as, e.g., triangles or voxels, while smaller-scale features usually are parameterized over the surface of the object. Such features include, but are not limited to, color details, small-scale surface-normal variations, or even view-dependent properties required for the light-surface interactions. This thesis is a collection of four papers that focus on new ways to compress and efficiently utilize surface data in 3D for real-time usage.In Paper IA and IB, we extend upon the concept of sparse voxel DAGs, a real-time compression format of a voxel-grid, to allow an attribute mapping with a negligible impact on the size. The main contribution, however, is a novel real-time compression format of the mapped colors over such sparse voxel surfaces.Paper II aims to utilize the results of the previous papers to achieve uv-free texturing of surfaces, such as triangle meshes, with optimized run-time minification as well as magnification filtering.Paper III extends upon previous compact representations of view dependent radiance using spherical gaussians (SG). By using a convolutional neural network, we are able to compress the light-field by finding SGs with free directions, amplitudes and sharpnesses, whereas previous methods were limited to only free amplitudes in similar scenarios

Relief mapping on cubic cell complexes

In this paper we present an algorithm for parameterizing arbitrary surfaces onto a quadrilateral domain defined by a collection of cubic cells. The parameterization inside each cell is implicit and thus requires storing no texture coordinates. Based upon this parameterization, we propose a unified representation of geometric and appearance information of complex models. The representation consists of a set of cubic cells (providing a coarse representation of the object) together with a collection of distance maps (encoding fine geometric detail inside each cell). Our new representation has similar uses than geometry images, but it requires storing a single distance value per texel instead of full vertex coordinates. When combined with color and normal maps, our representation can be used to render an approximation of the model through an output-sensitive relief mapping algorithm, thus being specially amenable for GPU raytracing.Postprint (author’s final draft

Physically-based interactive schlieren flow visualization

Journal ArticleUnderstanding fluid flow is a difficult problem and of increasing importance as computational fluid dynamics produces an abundance of simulation data. Experimental flow analysis has employed techniques such as shadowgraph and schlieren imaging for centuries which allow empirical observation of inhomogeneous flows. Shadowgraphs provide an intuitive way of looking at small changes in flow dynamics through caustic effects while schlieren cutoffs introduce an intensity gradation for observing large scale directional changes in the flow. The combination of these shading effects provides an informative global analysis of overall fluid flow. Computational solutions for these methods have proven too complex until recently due to the fundamental physical interaction of light refracting through the flow field. In this paper, we introduce a novel method to simulate the refraction of light to generate synthetic shadowgraphs and schlieren images of time-varying scalar fields derived from computational fluid dynamics (CFD) data. Our method computes physically accurate schlieren and shadowgraph images at interactive rates by utilizing a combination of GPGPU programming, acceleration methods, and data-dependent probabilistic schlieren cutoffs. Results comparing this method to previous schlieren approximations are presented

Many-Light Real-Time Global Illumination using Sparse Voxel Octree

Global illumination (GI) rendering simulates the propagation of light through a 3D volume and its interaction with surfaces, dramatically increasing the fidelity of computer generated images. While off-line GI algorithms such as ray tracing and radiosity can generate physically accurate images, their rendering speeds are too slow for real-time applications. The many-light method is one of many novel emerging real-time global illumination algorithms. However, it requires many shadow maps to be generated for Virtual Point Light (VPL) visibility tests, which reduces its efficiency. Prior solutions restrict either the number or accuracy of shadow map updates, which may lower the accuracy of indirect illumination or prevent the rendering of fully dynamic scenes. In this thesis, we propose a hybrid real-time GI algorithm that utilizes an efficient Sparse Voxel Octree (SVO) ray marching algorithm for visibility tests instead of the shadow map generation step of the many-light algorithm. Our technique achieves high rendering fidelity at about 50 FPS, is highly scalable and can support thousands of VPLs generated on the fly. A survey of current real-time GI techniques as well as details of our implementation using OpenGL and Shader Model 5 are also presented

Real-time hybrid cutting with dynamic fluid visualization for virtual surgery

It is widely accepted that a reform in medical teaching must be made to meet today's high volume training requirements. Virtual simulation offers a potential method of providing such trainings and some current medical training simulations integrate haptic and visual feedback to enhance procedure learning. The purpose of this project is to explore the capability of Virtual Reality (VR) technology to develop a training simulator for surgical cutting and bleeding in a general surgery

Doctor of Philosophy

dissertationVisualizing surfaces is a fundamental technique in computer science and is frequently used across a wide range of fields such as computer graphics, biology, engineering, and scientific visualization. In many cases, visualizing an interface between boundaries can provide meaningful analysis or simplification of complex data. Some examples include physical simulation for animation, multimaterial mesh extraction in biophysiology, flow on airfoils in aeronautics, and integral surfaces. However, the quest for high-quality visualization, coupled with increasingly complex data, comes with a high computational cost. Therefore, new techniques are needed to solve surface visualization problems within a reasonable amount of time while also providing sophisticated visuals that are meaningful to scientists and engineers. In this dissertation, novel techniques are presented to facilitate surface visualization. First, a particle system for mesh extraction is parallelized on the graphics processing unit (GPU) with a red-black update scheme to achieve an order of magnitude speed-up over a central processing unit (CPU) implementation. Next, extending the red-black technique to multiple materials showed inefficiencies on the GPU. Therefore, we borrow the underlying data structure from the closest point method, the closest point embedding, and the particle system solver is switched to hierarchical octree-based approach on the GPU. Third, to demonstrate that the closest point embedding is a fast, flexible data structure for surface particles, it is adapted to unsteady surface flow visualization at near-interactive speeds. Finally, the closest point embedding is a three-dimensional dense structure that does not scale well. Therefore, we introduce a closest point sparse octree that allows the closest point embedding to scale to higher resolution. Further, we demonstrate unsteady line integral convolution using the closest point method

Durability of Wireless Charging Systems Embedded Into Concrete Pavements for Electric Vehicles

Point clouds are widely used in various applications such as 3D modeling, geospatial analysis, robotics, and more. One of the key advantages of 3D point cloud data is that, unlike other data formats like texture, it is independent of viewing angle, surface type, and parameterization. Since each point in the point cloud is independent of the other, it makes it the most suitable source of data for tasks like object recognition, scene segmentation, and reconstruction. Point clouds are complex and verbose due to the numerous attributes they contain, many of which may not be always necessary for rendering, making retrieving and parsing a heavy task. As Sensors are becoming more precise and popular, effectively streaming, processing, and rendering the data is also becoming more challenging. In a hierarchical continuous LOD system, the previously fetched and rendered data for a region may become unavailable when revisiting it. To address this, we use a non-persistence cache using hash-map which stores the parsed point attributes, which still has some limitations, such as the dataset needing to be refetched and reprocessed if the tab or browser is closed and reopened which can be addressed by persistence caching. On the web, popularly persistence caching involves storing data in server memory, or an intermediate caching server like Redis. This is not suitable for point cloud data where we have to store parsed and processed large point data making point cloud visualization rely only on non-persistence caching. The thesis aims to contribute toward better performance and suitability of point cloud rendering on the web reducing the number of read requests to the remote file to access data.We achieve this with the application of client-side-based LRU Cache and Private File Open Space as a combination of both persistence and non-persistence caching of data. We use a cloud-optimized data format, which is better suited for web and streaming hierarchical data structures. Our focus is to improve rendering performance using WebGPU by reducing access time and minimizing the amount of data loaded in GPU. Preliminary results indicate that our approach significantly improves rendering performance and reduce network request when compared to traditional caching methods using WebGPU

A texture-based framework for improving CFD data visualization in a virtual environment

In the field of computational fluid dynamics (CFD) accurate representations of fluid phenomena can be simulated but require large amounts of data to represent the flow domain. Inefficient handling and access of the data at initialization and runtime can limit the ability of the engineering to quickly visualize and investigate the entire flow simulation, and thus hampering the ability to make a quality engineering decision in a timely manner. This problem is amplified n-fold if the solution set is time dependent, or transient. To visualize the data efficiently, dataset access should be decreased if not eliminated at runtime to provide an interactive environment to the end user. Also a reduction in the size of the initial datasets should be reduced as much as possible while maintaining validity of the solution so that larger (i.e. transient) solution datasets can be visualized. To accomplish this, the format in which the dataset is stored should be changed from conventional formats. With the recent advancements of graphical processor unit (GPU) technology, current research in the computer graphics community has lead a novel approach for efficiently storing and accessing flow field data as texture data during a visualization. A so-called texture-based solution for visualization of flow fields allows the end user to visualize complex three-dimensional flow fields in an intuitive fashion while remaining interactive. This work presents a framework for incorporating texture-based analysis techniques into a current CFD visualization application to improve the capabilities for investigating flow fields. The framework presented easily extendible to allow for research and incorporation of progressive visualization methods, in keeping with current technology. Comparisons of the current framework with the texture-based framework are shown to effectively visualize a dataset that could not be visualized in its entirety with the current framework. Comparisons of common visualization techniques, such as contour planes and streamlines, are made to show how the texture-based framework out performs the current framework

Hierarchical N-Body problem on graphics processor unit

Galactic simulation is an important cosmological computation, and represents a classical N-body problem suitable for implementation on vector processors. Barnes-Hut algorithm is a hierarchical N-Body method used to simulate such galactic evolution systems. Stream processing architectures expose data locality and concurrency available in multimedia applications. On the other hand, there are numerous compute-intensive scientific or engineering applications that can potentially benefit from such computational and communication models. These applications are traditionally implemented on vector processors. Stream architecture based graphics processor units (GPUs) present a novel computational alternative for efficiently implementing such high-performance applications. Rendering on a stream architecture sustains high performance, while user-programmable modules allow implementing complex algorithms efficiently. GPUs have evolved over the years, from being fixed-function pipelines to user programmable processors. In this thesis, we focus on the implementation of Barnes-Hut algorithm on typical current-generation programmable GPUs. We exploit computation and communication requirements present in Barnes-Hut algorithm to expose their suitability for user-programmable GPUs. Our implementation of the Barnes-Hut algorithm is formulated as a fragment shader targeting the selected GPU. We discuss implementation details, design issues, results, and challenges encountered in programming the fragment shader