132 research outputs found
Methods for Automated Creation and Efficient Visualisation of Large-Scale Terrains based on Real Height-Map Data
Real-time rendering of large-scale terrains is a difficult problem and remains an active field of research. The massive scale of these landscapes, where the ratio between the size of the terrain and its resolution is spanning multiple orders of magnitude, requires an efficient level of detail strategy. It is crucial that the geometry, as well as the terrain data, are represented seamlessly at varying distances while maintaining a constant visual quality. This thesis investigates common techniques and previous solutions to problems associated with the rendering of height field terrains and discusses their benefits and drawbacks. Subsequently, two solutions to the stated problems are presented, which build and expand upon the state-of-the-art rendering methods. A seamless and efficient mesh representation is achieved by the novel Uniform Distance-Dependent Level of Detail (UDLOD) triangulation method. This fully GPU-based algorithm subdivides a quadtree covering the terrain into small tiles, which can be culled in parallel, and are morphed seamlessly in the vertex shader, resulting in a densely and temporally consistent triangulated mesh. The proposed Chunked Clipmap combines the strengths of both quadtrees and clipmaps to enable efficient out-of-core paging of terrain data. This data structure allows for constant time view-dependent access, graceful degradation if data is unavailable, and supports trilinear and anisotropic filtering. Together these, otherwise independent, techniques enable the rendering of large-scale real-world terrains, which is demonstrated on a dataset encompassing the entire Free State of Saxony at a resolution of one meter, in real-time
Multiresolution Techniques for Real–Time Visualization of Urban Environments and Terrains
In recent times we are witnessing a steep increase in the availability of data coming from real–life environments.
Nowadays, virtually everyone connected to the Internet may have instant access to a tremendous amount of data coming from satellite elevation maps, airborne time-of-flight scanners and digital cameras, street–level photographs and even cadastral maps.
As for other, more traditional types of media such as pictures and videos, users of digital exploration softwares expect commodity hardware to exhibit good performance for interactive purposes, regardless of the dataset size.
In this thesis we propose novel solutions to the problem of rendering large terrain and urban models on commodity platforms, both for local and remote exploration.
Our solutions build on the concept of multiresolution representation, where alternative representations of the same data with different accuracy are used to selectively distribute the computational power, and consequently the visual accuracy, where it is more needed on the base of the user’s point of view.
In particular, we will introduce an efficient multiresolution data compression technique for planar and spherical surfaces applied to terrain datasets which is able to handle huge amount of information at a planetary scale.
We will also describe a novel data structure for compact storage and rendering of urban entities such as buildings to allow real–time exploration of cityscapes from a remote online repository.
Moreover, we will show how recent technologies can be exploited to transparently integrate virtual exploration and general computer graphics techniques with web applications
Scalable visualization of spatial data in 3D terrain
Designing visualizations of spatial data in 3D terrain is challenging because various heterogeneous data aspects need to be considered, including the terrain itself, multiple data attributes, and data uncertainty. It is hardly possible to visualize these data at full detail in a single image. Therefore, this thesis devises a scalable visualization approach that focuses on relevant information to be emphasized, while less-relevant information can be attenuated. In this context, a noval concept of visualizing spatial data in 3D terrain and different soft- and hardware solutions are proposed.Die Erstellung von Visualisierungen für räumliche Daten im 3D-Gelände ist schwierig, da viele heterogene Datenaspekte wie das Gelände selbst, die verschiedenen Datenattribute sowie Unsicherheiten bei der Darstellung zu berücksichtigen sind. Im Allgemeinen ist es nicht möglich, diese Datenaspekte gleichzeitig in einer Visualisierung darzustellen. Daher werden in der Arbeit skalierbare Visualisierungsstrategien entwickelt, welche die wichtigen Informationen hervorheben und trotzdem gleichzeitig Kontextinformationen liefern. Hierfür werden neue Systematisierungen und Konzepte vorgestellt
Proceedings, MSVSCC 2015
The Virginia Modeling, Analysis and Simulation Center (VMASC) of Old Dominion University hosted the 2015 Modeling, Simulation, & Visualization Student capstone Conference on April 16th. The Capstone Conference features students in Modeling and Simulation, undergraduates and graduate degree programs, and fields from many colleges and/or universities. Students present their research to an audience of fellow students, faculty, judges, and other distinguished guests. For the students, these presentations afford them the opportunity to impart their innovative research to members of the M&S community from academic, industry, and government backgrounds. Also participating in the conference are faculty and judges who have volunteered their time to impart direct support to their students’ research, facilitate the various conference tracks, serve as judges for each of the tracks, and provide overall assistance to this conference. 2015 marks the ninth year of the VMASC Capstone Conference for Modeling, Simulation and Visualization. This year our conference attracted a number of fine student written papers and presentations, resulting in a total of 51 research works that were presented. This year’s conference had record attendance thanks to the support from the various different departments at Old Dominion University, other local Universities, and the United States Military Academy, at West Point. We greatly appreciated all of the work and energy that has gone into this year’s conference, it truly was a highly collaborative effort that has resulted in a very successful symposium for the M&S community and all of those involved. Below you will find a brief summary of the best papers and best presentations with some simple statistics of the overall conference contribution. Followed by that is a table of contents that breaks down by conference track category with a copy of each included body of work. Thank you again for your time and your contribution as this conference is designed to continuously evolve and adapt to better suit the authors and M&S supporters.
Dr.Yuzhong Shen Graduate Program Director, MSVE Capstone Conference Chair
John ShullGraduate Student, MSVE Capstone Conference Student Chai
A Data-Virtualization System for Large Model Visualization
Interactive scientific visualizations are widely used for the visual exploration and examination of physical data resulting from measurements or simulations. Driven by technical advancements of data acquisition and simulation technologies, especially in the geo-scientific domain, large amounts of highly detailed subsurface data are generated. The oil and gas industry is particularly pushing such developments as hydrocarbon reservoirs are increasingly difficult to discover and exploit. Suitable visualization techniques are vital for the discovery of the reservoirs as well as their development and production. However, the ever-growing scale and complexity of geo-scientific data sets result in an expanding disparity between the size of the data and the capabilities of current computer systems with regard to limited memory and computing resources.
In this thesis we present a unified out-of-core data-virtualization system supporting geo-scientific data sets consisting of multiple large seismic volumes and height-field surfaces, wherein each data set may exceed the size of the graphics memory or possibly even the main memory. Current data sets fall within the range of hundreds of gigabytes up to terabytes in size. Through the mutual utilization of memory and bandwidth resources by multiple data sets, our data-management system is able to share and balance limited system resources among different data sets. We employ multi-resolution methods based on hierarchical octree and quadtree data structures to generate level-of-detail working sets of the data stored in main memory and graphics memory for rendering. The working set generation in our system is based on a common feedback mechanism with inherent support for translucent geometric and volumetric data sets. This feedback mechanism collects information about required levels of detail during the rendering process and is capable of directly resolving data visibility without the application of any costly occlusion culling approaches. A central goal of the proposed out-of-core data management system is an effective virtualization of large data sets. Through an abstraction of the level-of-detail working sets, our system allows developers to work with extremely large data sets independent of their complex internal data representations and physical memory layouts.
Based on this out-of-core data virtualization infrastructure, we present distinct rendering approaches for specific visualization problems of large geo-scientific data sets. We demonstrate the application of our data virtualization system and show how multi-resolution data can be treated exactly the same way as regular data sets during the rendering process. An efficient volume ray casting system is presented for the rendering of multiple arbitrarily overlapping multi-resolution volume data sets. Binary space-partitioning volume decomposition of the bounding boxes of the cube-shaped volumes is used to identify the overlapping and non-overlapping volume regions in order to optimize the rendering process. We further propose a ray casting-based rendering system for the visualization of geological subsurface models consisting of multiple very detailed height fields. The rendering of an entire stack of height-field surfaces is accomplished in a single rendering pass using a two-level acceleration structure, which combines a minimum-maximum quadtree for empty-space skipping and sorted lists of depth intervals to restrict ray intersection searches to relevant height fields and depth ranges. Ultimately, we present a unified rendering system for the visualization of entire geological models consisting of highly detailed stacked horizon surfaces and massive volume data. We demonstrate a single-pass ray casting approach facilitating correct visual interaction between distinct translucent model components, while increasing the rendering efficiency by reducing processing overhead of potentially invisible parts of the model. The combination of image-order rendering approaches and the level-of-detail feedback mechanism used by our out-of-core data-management system inherently accounts for occlusions of different data types without the application of costly culling techniques.
The unified out-of-core data-management and virtualization infrastructure considerably facilitates the implementation of complex visualization systems. We demonstrate its applicability for the visualization of large geo-scientific data sets using output-sensitive rendering techniques. As a result, the magnitude and multitude of data sets that can be interactively visualized is significantly increased compared to existing approaches
Real-time rendering of large surface-scanned range data natively on a GPU
This thesis presents research carried out for the visualisation of surface anatomy data stored as large range images such as those produced by stereo-photogrammetric, and other triangulation-based capture devices. As part of this research, I explored the use of points as a rendering primitive as opposed to polygons, and the use of range images as the native data representation.
Using points as a display primitive as opposed to polygons required the creation of a pipeline that solved problems associated with point-based rendering. The problems inves tigated were scattered-data interpolation (a common problem with point-based rendering), multi-view rendering, multi-resolution representations, anti-aliasing, and hidden-point re- moval. In addition, an efficient real-time implementation on the GPU was carried out
Towards Predictive Rendering in Virtual Reality
The strive for generating predictive images, i.e., images representing radiometrically correct renditions of reality, has been a longstanding problem in computer graphics. The exactness of such images is extremely important for Virtual Reality applications like Virtual Prototyping, where users need to make decisions impacting large investments based on the simulated images. Unfortunately, generation of predictive imagery is still an unsolved problem due to manifold reasons, especially if real-time restrictions apply. First, existing scenes used for rendering are not modeled accurately enough to create predictive images. Second, even with huge computational efforts existing rendering algorithms are not able to produce radiometrically correct images. Third, current display devices need to convert rendered images into some low-dimensional color space, which prohibits display of radiometrically correct images. Overcoming these limitations is the focus of current state-of-the-art research. This thesis also contributes to this task. First, it briefly introduces the necessary background and identifies the steps required for real-time predictive image generation. Then, existing techniques targeting these steps are presented and their limitations are pointed out. To solve some of the remaining problems, novel techniques are proposed. They cover various steps in the predictive image generation process, ranging from accurate scene modeling over efficient data representation to high-quality, real-time rendering. A special focus of this thesis lays on real-time generation of predictive images using bidirectional texture functions (BTFs), i.e., very accurate representations for spatially varying surface materials. The techniques proposed by this thesis enable efficient handling of BTFs by compressing the huge amount of data contained in this material representation, applying them to geometric surfaces using texture and BTF synthesis techniques, and rendering BTF covered objects in real-time. Further approaches proposed in this thesis target inclusion of real-time global illumination effects or more efficient rendering using novel level-of-detail representations for geometric objects. Finally, this thesis assesses the rendering quality achievable with BTF materials, indicating a significant increase in realism but also confirming the remainder of problems to be solved to achieve truly predictive image generation
Gaussian Processes for Machine Learning in Robotics
Mención Internacional en el tÃtulo de doctorNowadays, machine learning is widely used in robotics for a variety of tasks such as
perception, control, planning, and decision making. Machine learning involves learning,
reasoning, and acting based on the data. This is achieved by constructing computer
programs that process the data, extract useful information or features, make predictions to
infer unknown properties, and suggest actions to take or decisions to make. This computer
program corresponds to a mathematical model of the data that describes the relationship
between the variables that represent the observed data and properties of interest. The
aforementioned model is learned based on the available training data, which is accomplished
using a learning algorithm capable of automatically adjusting the parameters of
the model to agree with the data. Therefore, the architecture of the model needs to be selected
accordingly, which is not a trivial task and usually depends on the machine-learning
engineer’s insights and past experience. The number of parameters to be tuned varies significantly
with the selected machine learning model, ranging from two or three parameters
for Gaussian processes (GP) to hundreds of thousands for artificial neural networks.
However, as more complex and novel robotic applications emerge, data complexity
increases and prior experience may be insufficient to define adequate mathematical models.
In addition, traditional machine learning methods are prone to problems such as
overfitting, which can lead to inaccurate predictions and catastrophic failures in critical
applications. These methods provide probabilistic distributions as model outputs, allowing
for estimating the uncertainty associated with predictions and making more informed
decisions. That is, they provide a mean and variance for the model responses.
This thesis focuses on the application of machine learning solutions based on Gaussian
processes to various problems in robotics, with the aim of improving current methods and
providing a new perspective. Key areas such as trajectory planning for unmanned aerial
vehicles (UAVs), motion planning for robotic manipulators and model identification of
nonlinear systems are addressed. In the field of path planning for UAVs, algorithms based on Gaussian processes that
allow for more efficient planning and energy savings in exploration missions have been
developed. These algorithms are compared with traditional analytical approaches, demonstrating
their superiority in terms of efficiency when using machine learning. Area coverage
and linear coverage algorithms with UAV formations are presented, as well as a
sea surface search algorithm. Finally, these algorithms are compared with a new method
that uses Gaussian processes to perform probabilistic predictions and optimise trajectory
planning, resulting in improved performance and reduced energy consumption.
Regarding motion planning for robotic manipulators, an approach based on Gaussian
process models that provides a significant reduction in computational times is proposed.
A Gaussian process model is used to approximate the configuration space of a robot,
which provides valuable information to avoid collisions and improve safety in dynamic
environments. This approach is compared to conventional collision checking methods
and its effectiveness in terms of computational time and accuracy is demonstrated. In this
application, the variance provides information about dangerous zones for the manipulator.
In terms of creating models of non-linear systems, Gaussian processes also offer significant
advantages. This approach is applied to a soft robotic arm system and UAV energy
consumption models, where experimental data is used to train Gaussian process models
that capture the relationships between system inputs and outputs. The results show accurate
identification of system parameters and the ability to make reliable future predictions.
In summary, this thesis presents a variety of applications of Gaussian processes in
robotics, from trajectory and motion planning to model identification. These machine
learning-based solutions provide probabilistic predictions and improve the ability of robots
to perform tasks safely and efficiently. Gaussian processes are positioned as a powerful
tool to address current challenges in robotics and open up new possibilities in the field.El aprendizaje automático ha revolucionado el campo de la robótica al ofrecer una amplia
gama de aplicaciones en áreas como la percepción, el control, la planificación y la toma de
decisiones. Este enfoque implica desarrollar programas informáticos que pueden procesar
datos, extraer información valiosa, realizar predicciones y ofrecer recomendaciones o
sugerencias de acciones. Estos programas se basan en modelos matemáticos que capturan
las relaciones entre las variables que representan los datos observados y las propiedades
que se desean analizar. Los modelos se entrenan utilizando algoritmos de optimización
que ajustan automáticamente los parámetros para lograr un rendimiento óptimo.
Sin embargo, a medida que surgen aplicaciones robóticas más complejas y novedosas,
la complejidad de los datos aumenta y la experiencia previa puede resultar insuficiente
para definir modelos matemáticos adecuados. Además, los métodos de aprendizaje automático
tradicionales son propensos a problemas como el sobreajuste, lo que puede llevar
a predicciones inexactas y fallos catastróficos en aplicaciones crÃticas. Para superar estos
desafÃos, los métodos probabilÃsticos de aprendizaje automático, como los procesos
gaussianos, han ganado popularidad. Estos métodos ofrecen distribuciones probabilÃsticas
como salidas del modelo, lo que permite estimar la incertidumbre asociada a las
predicciones y tomar decisiones más informadas. Esto es, proporcionan una media y una
varianza para las respuestas del modelo.
Esta tesis se centra en la aplicación de soluciones de aprendizaje automático basadas
en procesos gaussianos a diversos problemas en robótica, con el objetivo de mejorar los
métodos actuales y proporcionar una nueva perspectiva. Se abordan áreas clave como la
planificación de trayectorias para vehÃculos aéreos no tripulados (UAVs), la planificación
de movimientos para manipuladores robóticos y la identificación de modelos de sistemas
no lineales.
En el campo de la planificación de trayectorias para UAVs, se han desarrollado algoritmos basados en procesos gaussianos que permiten una planificación más eficiente y
un ahorro de energÃa en misiones de exploración. Estos algoritmos se comparan con los
enfoques analÃticos tradicionales, demostrando su superioridad en términos de eficiencia
al utilizar el aprendizaje automático. Se presentan algoritmos de recubrimiento de áreas
y recubrimiento lineal con formaciones de UAVs, asà como un algoritmo de búsqueda
en superficies marinas. Finalmente, estos algoritmos se comparan con un nuevo método
que utiliza procesos gaussianos para realizar predicciones probabilÃsticas y optimizar la
planificación de trayectorias, lo que resulta en un rendimiento mejorado y una reducción
del consumo de energÃa.
En cuanto a la planificación de movimientos para manipuladores robóticos, se propone
un enfoque basado en modelos gaussianos que permite una reducción significativa
en los tiempos de cálculo. Se utiliza un modelo de procesos gaussianos para aproximar
el espacio de configuraciones de un robot, lo que proporciona información valiosa para
evitar colisiones y mejorar la seguridad en entornos dinámicos. Este enfoque se compara
con los métodos convencionales de planificación de movimientos y se demuestra su eficacia
en términos de tiempo de cálculo y precisión de los movimientos. En esta aplicación,
la varianza proporciona información sobre zonas peligrosas para el manipulador.
En cuanto a la identificación de modelos de sistemas no lineales, los procesos gaussianos
también ofrecen ventajas significativas. Este enfoque se aplica a un sistema de
brazo robótico blando y a modelos de consumo energético de UAVs, donde se utilizan
datos experimentales para entrenar un modelo de proceso gaussiano que captura las relaciones
entre las entradas y las salidas del sistema. Los resultados muestran una identificación
precisa de los parámetros del sistema y la capacidad de realizar predicciones
futuras confiables.
En resumen, esta tesis presenta una variedad de aplicaciones de procesos gaussianos
en robótica, desde la planificación de trayectorias y movimientos hasta la identificación
de modelos. Estas soluciones basadas en aprendizaje automático ofrecen predicciones
probabilÃsticas y mejoran la capacidad de los robots para realizar tareas de manera segura
y eficiente. Los procesos gaussianos se posicionan como una herramienta poderosa para
abordar los desafÃos actuales en robótica y abrir nuevas posibilidades en el campo.Programa de Doctorado en IngenierÃa Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Juan Jesús Romero Cardalda.- Secretaria: MarÃa Dolores Blanco Rojas.- Vocal: Giuseppe Carbon
Methods for Real-time Visualization and Interaction with Landforms
This thesis presents methods to enrich data modeling and analysis in the geoscience domain with a particular focus on geomorphological applications. First, a short overview of the relevant characteristics of the used remote sensing data and basics of its processing and visualization are provided. Then, two new methods for the visualization of vector-based maps on digital elevation models (DEMs) are presented. The first method uses a texture-based approach that generates a texture from the input maps at runtime taking into account the current viewpoint. In contrast to that, the second method utilizes the stencil buffer to create a mask in image space that is then used to render the map on top of the DEM. A particular challenge in this context is posed by the view-dependent level-of-detail representation of the terrain geometry. After suitable visualization methods for vector-based maps have been investigated, two landform mapping tools for the interactive generation of such maps are presented. The user can carry out the mapping directly on the textured digital elevation model and thus benefit from the 3D visualization of the relief. Additionally, semi-automatic image segmentation techniques are applied in order to reduce the amount of user interaction required and thus make the mapping process more efficient and convenient. The challenge in the adaption of the methods lies in the transfer of the algorithms to the quadtree representation of the data and in the application of out-of-core and hierarchical methods to ensure interactive performance. Although high-resolution remote sensing data are often available today, their effective resolution at steep slopes is rather low due to the oblique acquisition angle. For this reason, remote sensing data are suitable to only a limited extent for visualization as well as landform mapping purposes. To provide an easy way to supply additional imagery, an algorithm for registering uncalibrated photos to a textured digital elevation model is presented. A particular challenge in registering the images is posed by large variations in the photos concerning resolution, lighting conditions, seasonal changes, etc. The registered photos can be used to increase the visual quality of the textured DEM, in particular at steep slopes. To this end, a method is presented that combines several georegistered photos to textures for the DEM. The difficulty in this compositing process is to create a consistent appearance and avoid visible seams between the photos. In addition to that, the photos also provide valuable means to improve landform mapping. To this end, an extension of the landform mapping methods is presented that allows the utilization of the registered photos during mapping. This way, a detailed and exact mapping becomes feasible even at steep slopes
- …