Volume visualization has been made available on the web using the Direct Volume Rendering (DVR) technique, powered by the WebGL 1 API. While the technique produces visually pleasing output, the performance of the prototypes that implement this leave much desired. 2017 saw the release of the next version of WebGL, WebGL 2.0 and the introduction of WebAsssembly. These APIs and formats are promising tools for formulating a DVR application that can do high performance rendering at interactive frame rates. This thesis investigates, implements and evaluates a prototype application that utilizes the optimization methods of Adaptive Texture Maps, Octree Empty Space Skipping and Distance Transform Empty Space Skipping. The Distance Transform is further evaluated by a CPU bound and a GPU bound algorithm implementation. The techniques are assessed on readily available off the shelf devices and hardware. The performance of the prototype application ran on these devices is quantified by measuring computation times of costly operations, and measuring frames per second. It is concluded that for different hardware, the methods have different properties. While higher FPS is achieved for all devices by utilizing some combination of the optimization methods, the distance transform is the most consistent. A discussion on embedded devices and their quirks is also held, where memory constraints and the resolution of the data is of greater importance than on the non-embedded devices. This results in some suggested actions that can be taken to also potentially enable high-performance rendering of higher resolution data on these devices.

<p>3D fractals can be visualized as 3D objects with complex structure and has unlimited details. This thesis will be about methods to render 3D fractals effectively and efficiently, both to explore it in real-time and to create beautiful high resolution images with high details. The methods discussed is direct volume rendering with ray-casting and cut plane rendering to explore the fractal and an approach that uses super sampling to create high resolution images. Stereoscopic rendering is discussed and how it enhance the visual perception of the fractal</p>

In the last decade, computational power, memory bandwidth and programmability capabilities of graphics processing units (GPU) have rapidly evolved. Therefore, many researches have been performed to use GPUs in advanced graphics rendering. Because of its high degree of parallelism, ray tracing has been one of the rst algorithms studied on GPUs. However, the rendering of large scenes with ray tracing can easily exceed the GPU&#039 / s memory capacity. The algorithm proposed in this work uses a data parallel approach where the scene is partitioned and assigned to CPU-GPU couples in a cluster to overcome this problem. Our algorithm focuses on ray casting which is a special case of ray tracing mainly used in visualization of volumetric data. CPUs are pretty ecient in ow control and branching while GPUs are very fast performing intense oating point operations. Using these facts, the GPUs in the cluster are assigned the task of performing ray casting while the CPUs are responsible for traversing the rays. In the end, we were able to visualize large scenes successfully by utilizing CPU-GPU couples eectively and observed that the performance is highly dependent on the viewing angle as a result of load imbalance.

This work serves to present the background, approach, and selected results for the initial master thesis and prototyping phase of Open Space, a joint visualization software development project by National Aeronautics and Space Administration (NASA), Linköping University (LiU) and the American Museum of Natural History (AMNH). The thesis report provides a theoretical introduction to heliophysics, modeling of space weather events, volumetric rendering, and an understanding of how these relate in the bigger scope of Open Space. A set of visualization tools that are currently used at NASA and AMNH are presented and discussed. These tools are used to visualize global heliosphere models, both for scientific studies and for public presentations, and are mainly making use of geometric rendering techniques. The paper will, in detail, describe a new approach to visualize the science models with volumetric rendering to better represent the volumetric structure of the data. Custom processors have been developed for the open source volumetric rendering engine Voreen, to load and visualize science models provided by the Community Coordinated Modeling Center (CCMC) at NASA Goddard Space Flight Center (GSFC). Selected parts of the code are presented by C++ code examples. To best represent models that are defined in non-Cartesian space, a new approach to volumetric rendering is presented and discussed. Compared to the traditional approach of transforming such models to Cartesian space, this new approach performs no such model transformations, and thus minimizes the amount of empty voxels and introduces less interpolation artifacts. Final results are presented as rendered images and are discussed from a scientific visualization perspective, taking into account the physics representation, potential rendering artifacts, and the rendering performance.

The growing amount of photovoltaic systems in urban environments creates peaks of energy generation in local energy grids. These peaks can lead to unwanted instability in the electrical grid. By aligning solar panels differently, spikes could be avoided. Planning locations for solar panels in urban environments is very time-intense as they require a high spatial and temporal resolution. The aim of this thesis is to investigate the decrease in runtime of planning applications by parallelizing ray-casting algorithms. This thesis includes a software tool for professionals and laymen, which has been developed in a user centered design process and shows ways to perform those calculations on a graphics processing unit.After creating a computational concept and a concept of the software design, those concepts have been implemented starting with an implementation of the Möller-Trumbore ray-casting algorithm which has been run with Python on the central processing unit (CPU). Further the same test with the same algorithm and the same data has been performed on the graphics processing unit (GPU) by using PyCUDA, a Python wrapper for NVIDIAs Compute Unified Device Architecture (CUDA). Both results were compared resulting in, that parallelizing, transferring and performing those calculations on the graphics processing unit can decrease the runtime of a software significantly. In the used system setup, the same calculations were 42 times faster on the Graphics Processing Unit than on the Central Processing Unit. It was also found, that other factors such as the time of the year, the location of the tested points in the data model, the test interval length and the algorithm design of the ray-casting algorithm have a major impact on the performance of such. In the test scenario the processing time for the same case, but just during another time of the year, increases by factor 4.The findings of this thesis can be used in a wide range of software as it shows, that computationally intensive calculations can easily be sourced out from the Python code and executed on another platform. By doing so, the runtime can be significantly decreased and the whole software package can get an enormous speed boost.

3D fractals can be visualized as 3D objects with complex structure and has unlimited details. This thesis will be about methods to render 3D fractals effectively and efficiently, both to explore it in real-time and to create beautiful high resolution images with high details. The methods discussed is direct volume rendering with ray-casting and cut plane rendering to explore the fractal and an approach that uses super sampling to create high resolution images. Stereoscopic rendering is discussed and how it enhance the visual perception of the fractal

This thesis deals with accelerated 3D rendering of medical data, e.g. computed tomography, using a graphics processor and OpenGL library. Raw data slices are send to graphic memory and rendered by a ray-casting algorithm. The goal of this project is high quality visual output and full user interaction at the same time. Multiple rendering modes are avaiable to the user: MIP, X-Ray simulation and realistic shading.

Synthetic Vision Systems (SVS) provide an aircraft pilot with a virtual 3-D image of surrounding terrain which is generated from a digital elevation model stored in an onboard database. SVS improves the pilot's situational awareness at night and in inclement weather, thus reducing the chance of accidents such as controlled flight into terrain. A terrain database integrity monitor is needed to verify the accuracy of the displayed image due to potential database and navigational system errors. Previous research has used existing aircraft sensors to compare the real terrain position with the predicted position. We propose an improvement to one of these models by leveraging the stream computing capabilities of commercial graphics hardware. "Brook for GPUs," a system for implementing stream computing applications on programmable graphics processors, is used to execute a streaming ray-casting algorithm that correctly simulates the beam characteristics of a radar altimeter during all phases of flight.

Cartesian grids offer a valuable option to simulate aerodynamic flows around complex geometries such as multi-element airfoils, aircrafts, and rockets. Therefore, an adaptively-refined Cartesian grid generator and Euler solver are developed. For the mesh generation part of the algorithm, dynamic data structures are used to determine connectivity information between cells and uniform mesh is created in the domain. Marching squares and cubes algorithms are used to form interfaces of cut and split cells. Geometry-based cell adaptation is applied in the mesh generation. After obtaining appropriate mesh around input geometry, the solution is obtained using either flux vector splitting method or Roe&rsquo / s approximate Riemann solver with cell-centered approach. Least squares reconstruction of flow variables within the cell is used to determine high gradient regions of flow. Solution based adaptation method is then applied to current mesh in order to refine these regions and also coarsened regions where unnecessary small cells exist. Multistage time stepping is used with local time steps to increase the convergence rate. Also FAS multigrid technique is used in order to increase the convergence rate. It is obvious that implementation of geometry and solution based adaptations are easier for Cartesian meshes than other types of meshes. Besides, presented numerical results show the accuracy and efficiency of the algorithm by especially using geometry and solution based adaptation. Finally, Euler solutions of Cartesian grids around airfoils, projectiles and wings are compared with the experimental and numerical data available in the literature and accuracy and efficiency of the solver are verified.

Simultaneous localization and mapping is the technique to construct a 3D map of unknown environment. With the increasing popularity of RGB-depth (RGB-D) sensors such as the Microsoft Kinect, there have been much research on capturing and reconstructing 3D environments using a movable RGB-D sensor. The key process behind these kinds of simultaneous location and mapping (SLAM) systems is the iterative closest point or ICP algorithm, which is an iterative algorithm that can estimate the rigid movement of the camera based on the captured 3D point clouds. While ICP is a well-studied algorithm, it is problematic when it is used in scanning large planar regions such as wall surfaces in a room. The lack of depth variations on planar surfaces makes the global alignment an ill-conditioned problem. In this thesis, we present a novel approach for registering 3D point clouds by combining both color and depth information. Instead of directly searching for point correspondences among 3D data, the proposed method first extracts features from the RGB images, and then back-projects the features to the 3D space to identify more reliable correspondences. These color correspondences form the initial input to the ICP procedure which then proceeds to refine the alignment. Experimental results show that our proposed approach can achieve better accuracy than existing SLAMs in reconstructing indoor environments with large planar surfaces.