An out-of-core method for GPU image mapping on large 3D scenarios of the real world

[Abstract] Image mapping on 3D huge scenarios of the real world is one of the most fundamental and computational expensive processes for the integration of multi-source sensing data. Recent studies focused on the observation and characterization of Earth have been enhanced by the proliferation of Unmanned Aerial Vehicle (UAV) and sensors able to capture massive datasets with a high spatial resolution. Despite the advances in manufacturing new cameras and versatile platforms, only a few methods have been developed to characterize the study area by fusing heterogeneous data such as thermal, multispectral or hyperspectral images with high-resolution 3D models. The main reason for this lack of solutions is the challenge to integrate multi-scale datasets and high computational efforts required for image mapping on dense and complex geometric models. In this paper, we propose an efficient pipeline for multi-source image mapping on huge 3D scenarios. Our GPU-based solution significantly reduces the run time and allows us to generate enriched 3D models on-site. The proposed method is out-of-core and it uses available resources of the GPU’s machine to perform two main tasks: (i) image mapping and (ii) occlusion testing. We deploy highly-optimized GPU-kernels for image mapping and detection of self-hidden geometry in the 3D model, as well as a GPU-based parallelization to manage the 3D model considering several spatial partitions according to the GPU capabilities. Our method has been tested on 3D scenarios with different point cloud densities (66M, 271M, 542M) and two sets of multispectral images collected by two drone flights. We focus on launching the proposed method on three platforms: (i) System on a Chip (SoC), (ii) a user-grade laptop and (iii) a PC. The results demonstrate the method’s capabilities in terms of performance and versatility to be computed by commodity hardware. Thus, taking advantage of GPUs, this method opens the door for embedded and edge computing devices for 3D image mapping on large-scale scenarios in near real-time.This work has been partially supported through the research projects TIN2017-84968-R, PID2019-104184RB-I00 funded by MCIN/AEI/10.13039/501100011033 and ERDF funds “A way of doing Europe”, as well as by ED431C 2021/30, ED431F 2021/11 funded by Xunta de Galicia and 1381202 by Junta de AndalucíaXunta de Galicia; ED431C 2021/30Xunta de Galicia; ED431F 2021/11Junta de Andalucía; 138120

An interest point based illumination condition matching approach to photometric registration within augmented reality worlds

With recent and continued increases in computing power, and advances in the field of computer graphics, realistic augmented reality environments can now offer inexpensive and powerful solutions in a whole range of training, simulation and leisure applications. One key challenge to maintaining convincing augmentation, and therefore user immersion, is ensuring consistent illumination conditions between virtual and real environments, so that objects appear to be lit by the same light sources. This research demonstrates how real world lighting conditions can be determined from the two-dimensional view of the user. Virtual objects can then be illuminated and virtual shadows cast using these conditions. This new technique uses pairs of interest points from real objects and the shadows that they cast, viewed from a binocular perspective, to determine the position of the illuminant. This research has been initially focused on single point light sources in order to show the potential of the technique and has investigated the relationships between the many parameters of the vision system. Optimal conditions have been discovered by mapping the results of experimentally varying parameters such as FoV, camera angle and pose, image resolution, aspect ratio and illuminant distance. The technique is able to provide increased robustness where greater resolution imagery is used. Under optimal conditions it is possible to derive the position of a real world light source with low average error. An investigation of available literature has revealed that other techniques can be inflexible, slow, or disrupt scene realism. This technique is able to locate and track a moving illuminant within an unconstrained, dynamic world without the use of artificial calibration objects that would disrupt scene realism. The technique operates in real-time as the new algorithms are of low computational complexity. This allows high framerates to be maintained within augmented reality applications. Illuminant updates occur several times a second on an average to high end desktop computer. Future work will investigate the automatic identification and selection of pairs of interest points and the exploration of global illuminant conditions. The latter will include an analysis of more complex scenes and the consideration of multiple and varied light sources.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Visualization challenges in distributed heterogeneous computing environments

Large-scale computing environments are important for many aspects of modern life. They drive scientific research in biology and physics, facilitate industrial rapid prototyping, and provide information relevant to everyday life such as weather forecasts. Their computational power grows steadily to provide faster response times and to satisfy the demand for higher complexity in simulation models as well as more details and higher resolutions in visualizations. For some years now, the prevailing trend for these large systems is the utilization of additional processors, like graphics processing units. These heterogeneous systems, that employ more than one kind of processor, are becoming increasingly widespread since they provide many benefits, like higher performance or increased energy efficiency. At the same time, they are more challenging and complex to use because the various processing units differ in their architecture and programming model. This heterogeneity is often addressed by abstraction but existing approaches often entail restrictions or are not universally applicable. As these systems also grow in size and complexity, they become more prone to errors and failures. Therefore, developers and users become more interested in resilience besides traditional aspects, like performance and usability. While fault tolerance is well researched in general, it is mostly dismissed in distributed visualization or not adapted to its special requirements. Finally, analysis and tuning of these systems and their software is required to assess their status and to improve their performance. The available tools and methods to capture and evaluate the necessary information are often isolated from the context or not designed for interactive use cases. These problems are amplified in heterogeneous computing environments, since more data is available and required for the analysis. Additionally, real-time feedback is required in distributed visualization to correlate user interactions to performance characteristics and to decide on the validity and correctness of the data and its visualization. This thesis presents contributions to all of these aspects. Two approaches to abstraction are explored for general purpose computing on graphics processing units and visualization in heterogeneous computing environments. The first approach hides details of different processing units and allows using them in a unified manner. The second approach employs per-pixel linked lists as a generic framework for compositing and simplifying order-independent transparency for distributed visualization. Traditional methods for fault tolerance in high performance computing systems are discussed in the context of distributed visualization. On this basis, strategies for fault-tolerant distributed visualization are derived and organized in a taxonomy. Example implementations of these strategies, their trade-offs, and resulting implications are discussed. For analysis, local graph exploration and tuning of volume visualization are evaluated. Challenges in dense graphs like visual clutter, ambiguity, and inclusion of additional attributes are tackled in node-link diagrams using a lens metaphor as well as supplementary views. An exploratory approach for performance analysis and tuning of parallel volume visualization on a large, high-resolution display is evaluated. This thesis takes a broader look at the issues of distributed visualization on large displays and heterogeneous computing environments for the first time. While the presented approaches all solve individual challenges and are successfully employed in this context, their joint utility form a solid basis for future research in this young field. In its entirety, this thesis presents building blocks for robust distributed visualization on current and future heterogeneous visualization environments.Große Rechenumgebungen sind für viele Aspekte des modernen Lebens wichtig. Sie treiben wissenschaftliche Forschung in Biologie und Physik, ermöglichen die rasche Entwicklung von Prototypen in der Industrie und stellen wichtige Informationen für das tägliche Leben, beispielsweise Wettervorhersagen, bereit. Ihre Rechenleistung steigt stetig, um Resultate schneller zu berechnen und dem Wunsch nach komplexeren Simulationsmodellen sowie höheren Auflösungen in der Visualisierung nachzukommen. Seit einigen Jahren ist die Nutzung von zusätzlichen Prozessoren, z.B. Grafikprozessoren, der vorherrschende Trend für diese Systeme. Diese heterogenen Systeme, welche mehr als eine Art von Prozessor verwenden, finden zunehmend mehr Verbreitung, da sie viele Vorzüge, wie höhere Leistung oder erhöhte Energieeffizienz, bieten. Gleichzeitig sind diese jedoch aufwendiger und komplexer in der Nutzung, da die verschiedenen Prozessoren sich in Architektur und Programmiermodel unterscheiden. Diese Heterogenität wird oft durch Abstraktion angegangen, aber bisherige Ansätze sind häufig nicht universal anwendbar oder bringen Einschränkungen mit sich. Diese Systeme werden zusätzlich anfälliger für Fehler und Ausfälle, da ihre Größe und Komplexität zunimmt. Entwickler sind daher neben traditionellen Aspekten, wie Leistung und Bedienbarkeit, zunehmend an Widerstandfähigkeit gegenüber Fehlern und Ausfällen interessiert. Obwohl Fehlertoleranz im Allgemeinen gut untersucht ist, wird diese in der verteilten Visualisierung oft ignoriert oder nicht auf die speziellen Umstände dieses Feldes angepasst. Analyse und Optimierung dieser Systeme und ihrer Software ist notwendig, um deren Zustand einzuschätzen und ihre Leistung zu verbessern. Die verfügbaren Werkzeuge und Methoden, um die erforderlichen Informationen zu sammeln und auszuwerten, sind oft vom Kontext entkoppelt oder nicht für interaktive Szenarien ausgelegt. Diese Probleme sind in heterogenen Rechenumgebungen verstärkt, da dort mehr Daten für die Analyse verfügbar und notwendig sind. Für verteilte Visualisierung ist zusätzlich Rückmeldung in Echtzeit notwendig, um Interaktionen der Benutzer mit Leistungscharakteristika zu korrelieren und um die Gültigkeit und Korrektheit der Daten und ihrer Visualisierung zu entscheiden. Diese Dissertation präsentiert Beiträge für all diese Aspekte. Zunächst werden zwei Ansätze zur Abstraktion im Kontext von generischen Berechnungen auf Grafikprozessoren und Visualisierung in heterogenen Umgebungen untersucht. Der erste Ansatz verbirgt Details verschiedener Prozessoren und ermöglicht deren Nutzung über einheitliche Schnittstellen. Der zweite Ansatz verwendet pro-Pixel verkettete Listen (per-pixel linked lists) zur Kombination von Pixelfarben und zur Vereinfachung von ordnungsunabhängiger Transparenz in verteilter Visualisierung. Übliche Fehlertoleranz-Methoden im Hochleistungsrechnen werden im Kontext der verteilten Visualisierung diskutiert. Auf dieser Grundlage werden Strategien für fehlertolerante verteilte Visualisierung abgeleitet und in einer Taxonomie organisiert. Beispielhafte Umsetzungen dieser Strategien, ihre Kompromisse und Zugeständnisse, und die daraus resultierenden Implikationen werden diskutiert. Zur Analyse werden lokale Exploration von Graphen und die Optimierung von Volumenvisualisierung untersucht. Herausforderungen in dichten Graphen wie visuelle Überladung, Ambiguität und Einbindung zusätzlicher Attribute werden in Knoten-Kanten Diagrammen mit einer Linsenmetapher sowie ergänzenden Ansichten der Daten angegangen. Ein explorativer Ansatz zur Leistungsanalyse und Optimierung paralleler Volumenvisualisierung auf einer großen, hochaufgelösten Anzeige wird untersucht. Diese Dissertation betrachtet zum ersten Mal Fragen der verteilten Visualisierung auf großen Anzeigen und heterogenen Rechenumgebungen in einem größeren Kontext. Während jeder vorgestellte Ansatz individuelle Herausforderungen löst und erfolgreich in diesem Zusammenhang eingesetzt wurde, bilden alle gemeinsam eine solide Basis für künftige Forschung in diesem jungen Feld. In ihrer Gesamtheit präsentiert diese Dissertation Bausteine für robuste verteilte Visualisierung auf aktuellen und künftigen heterogenen Visualisierungsumgebungen

Heterogeneous Computing with Focus on Mechanical Engineering

During the past few years there has been a revolution in the design of desktop computers. Most processors today include more than one processor core, allowing parallel execution of programs. Furthermore, most commodity computers include a graphical processor that outperforms the central processor by at least one order of magnitude. Tapping into this vast resource is commonly referred to as heterogeneous computing. The change in hardware invalidates old software-design truths. There is therefore need for new algorithms, and research into adapting existing algorithms to these architectures. Our main focus has been to accelerate algorithms relevant for mechanical engineering. In this dissertation we present four algorithms devoted to take advantage of the computational strengths of heterogeneous architectures. Each work is based on state-of-the-art hardware available at the time the research was performed. First we describe an algorithm for high-quality visualization of parametric surfaces. This is useful in a CAD setting, were an accurate rendering is important for visual validation of model quality. We further describe simulation of shallow-water waves using a state-of-the-art numerical scheme. Our accelerated implementation gave a speedup of up to 40 times compared to an optimized reference implementation. Our implementation features real time simulation and visualization of semi-realistic nonlinear wave effects. Finally we present two algorithms for shape simplification of 3D-models. The algorithms aim at reducing time spent on preparing models for finite element analysis. Finite element analysis is important to determine mechanical properties of objects prior to manufacture. Such analysis can be used to investigate thermal behavior and determine the strengths and weaknesses of physical components. Before the analysis can take place the models must undergo a preparation phase where shape simplification plays an important role. The first work we describe for shape simplification is a hybrid algorithm, using graphics hardware for the computationally demanding operations, and the main processor for maintaining the data structure. Our second work describes a shape simplification algorithm highly suitable for heterogeneous architectures and a reference implementation on the Cell BE

Reducing redundancy of real time computer graphics in mobile systems

The goal of this thesis is to propose novel and effective techniques to eliminate redundant computations that waste energy and are performed in real-time computer graphics applications, with special focus on mobile GPU micro-architecture. Improving the energy-efficiency of CPU/GPU systems is not only key to enlarge their battery life, but also allows to increase their performance because, to avoid overheating above thermal limits, SoCs tend to be throttled when the load is high for a large period of time. Prior studies pointed out that the CPU and especially the GPU are the principal energy consumers in the graphics subsystem, being the off-chip main memory accesses and the processors inside the GPU the primary energy consumers of the graphics subsystem. First, we focus on reducing redundant fragment processing computations by means of improving the culling of hidden surfaces. During real-time graphics rendering, objects are processed by the GPU in the order they are submitted by the CPU, and occluded surfaces are often processed even though they will end up not being part of the final image. When the GPU realizes that an object or part of it is not going to be visible, all activity required to compute its color and store it has already been performed. We propose a novel architectural technique for mobile GPUs, Visibility Rendering Order (VRO), which reorders objects front-to-back entirely in hardware to maximize the culling effectiveness of the GPU and minimize overshading, hence reducing execution time and energy consumption. VRO exploits the fact that the objects in graphics animated applications tend to keep its relative depth order across consecutive frames (temporal coherence) to provide the feeling of smooth transition. VRO keeps visibility information of a frame, and uses it to reorder the objects of the following frame. VRO just requires adding a small hardware to capture the visibility information and use it later to guide the rendering of the following frame. Moreover, VRO works in parallel with the graphics pipeline, so negligible performance overheads are incurred. We illustrate the benefits of VRO using various unmodified commercial 3D applications for which VRO achieves 27% speed-up and 14.8% energy reduction on average. Then, we focus on avoiding redundant computations related to CPU Collision Detection (CD). Graphics applications such as 3D games represent a large percentage of downloaded applications for mobile devices and the trend is towards more complex and realistic scenes with accurate 3D physics simulations. CD is one of the most important algorithms in any physics kernel since it identifies the contact points between the objects of a scene and determines when they collide. However, real-time accurate CD is very expensive in terms of energy consumption. We propose Render Based Collision Detection (RBCD), a novel energy-efficient high-fidelity CD scheme that leverages some intermediate results of the rendering pipeline to perform CD, so that redundant tasks are done just once. Comparing RBCD with a conventional CD completely executed in the CPU, we show that its execution time is reduced by almost three orders of magnitude (600x speedup), because most of the CD task of our model comes for free by reusing the image rendering intermediate results. Although not necessarily, such a dramatic time improvement may result in better frames per second if physics simulation stays in the critical path. However, the most important advantage of our technique is the enormous energy savings that result from eliminating a long and costly CPU computation and converting it into a few simple operations executed by a specialized hardware within the GPU. Our results show that the energy consumed by CD is reduced on average by a factor of 448x (i.e., by 99.8\%). These dramatic benefits are accompanied by a higher fidelity CD analysis (i.e., with finer granularity), which improves the quality and realism of the application.El objetivo de esta tesis es proponer técnicas efectivas y originales para eliminar computaciones inútiles que aparecen en aplicaciones gráficas, con especial énfasis en micro-arquitectura de GPUs. Mejorar la eficiencia energética de los sistemas CPU/GPU no es solo clave para alargar la vida de la batería, sino también incrementar su rendimiento. Estudios previos han apuntado que la CPU y especialmente la GPU son los principales consumidores de energía en el sub-sistema gráfico, siendo los accesos a memoria off-chip y los procesadores dentro de la GPU los principales consumidores de energía del sub-sistema gráfico. Primero, nos hemos centrado en reducir computaciones redundantes de la fase de fragment processing mediante la mejora en la eliminación de superficies ocultas. Durante el renderizado de gráficos en tiempo real, los objetos son procesados por la GPU en el orden en el que son enviados por la CPU, y las superficies ocultas son a menudo procesadas incluso si no no acaban formando parte de la imagen final. Cuando la GPU averigua que el objeto o parte de él no es visible, toda la actividad requerida para computar su color y guardarlo ha sido realizada. Proponemos una técnica arquitectónica original para GPUs móviles, Visibility Rendering Order (VRO), la cual reordena los objetos de delante hacia atrás por completo en hardware para maximizar la efectividad del culling de la GPU y así minimizar el overshading, y por lo tanto reducir el tiempo de ejecución y el consumo de energía. VRO explota el hecho de que los objetos de las aplicaciones gráficas animadas tienden a mantener su orden relativo en profundidad a través de frames consecutivos (coherencia temporal) para proveer animaciones con transiciones suaves. Dado que las relaciones de orden en profundidad entre objetos son testeadas en la GPU, VRO introduce costes mínimos en energía. Solo requiere añadir una pequeña unidad hardware para capturar la información de visibilidad. Además, VRO trabaja en paralelo con el pipeline gráfico, por lo que introduce costes insignificantes en tiempo. Ilustramos los beneficios de VRO usango varias aplicaciones 3D comerciales para las cuales VRO consigue un 27% de speed-up y un 14.8% de reducción de energía en media. En segundo lugar, evitamos computaciones redundantes relacionadas con la Detección de Colisiones (CD) en la CPU. Las aplicaciones gráficas animadas como los juegos 3D representan un alto porcentaje de las aplicaciones descargadas en dispositivos móviles y la tendencia es hacia escenas más complejas y realistas con simulaciones físicas 3D precisas. La CD es uno de los algoritmos más importantes entre los kernel de físicas dado que identifica los puntos de contacto entre los objetos de una escena. Sin embargo, una CD en tiempo real y precisa es muy costosa en términos de consumo energético. Proponemos Render Based Collision Detection (RBCD), una técnica energéticamente eficiente y preciso de CD que utiliza resultados intermedios del rendering pipeline para realizar la CD. Comparando RBCD con una CD convencional completamente ejecutada en la CPU, mostramos que el tiempo de ejecución es reducido casi tres órdenes de magnitud (600x speedup), porque la mayoría de la CD de nuestro modelo reusa resultados intermedios del renderizado de la imagen. Aunque no es así necesariamente, esta espectacular en tiempo puede resultar en mejores frames por segundo si la simulación de físicas está en el camino crítico. Sin embargo, la ventaja más importante de nuestra técnica es el enorme ahorro de energía que resulta de eliminar las largas y costosas computaciones en la CPU, sustituyéndolas por unas pocas operaciones ejecutadas en un hardware especializado dentro de la GPU. Nuestros resultados muestran que la energía consumida por la CD es reducidad en media por un factor de 448x. Estos dramáticos beneficios vienen acompañados de una mayor fidelidad en la CD (i.e. con granularidad más fina)Postprint (published version