Hardware Acceleration of Progressive Refinement Radiosity using Nvidia RTX

A vital component of photo-realistic image synthesis is the simulation of indirect diffuse reflections, which still remain a quintessential hurdle that modern rendering engines struggle to overcome. Real-time applications typically pre-generate diffuse lighting information offline using radiosity to avoid performing costly computations at run-time. In this thesis we present a variant of progressive refinement radiosity that utilizes Nvidia's novel RTX technology to accelerate the process of form-factor computation without compromising on visual fidelity. Through a modern implementation built on DirectX 12 we demonstrate that offloading radiosity's visibility component to RT cores significantly improves the lightmap generation process and potentially propels it into the domain of real-time.Comment: 114 page

Ray Tracing Gems

This book is a must-have for anyone serious about rendering in real time. With the announcement of new ray tracing APIs and hardware to support them, developers can easily create real-time applications with ray tracing as a core component. As ray tracing on the GPU becomes faster, it will play a more central role in real-time rendering. Ray Tracing Gems provides key building blocks for developers of games, architectural applications, visualizations, and more. Experts in rendering share their knowledge by explaining everything from nitty-gritty techniques that will improve any ray tracer to mastery of the new capabilities of current and future hardware. What you'll learn: The latest ray tracing techniques for developing real-time applications in multiple domains Guidance, advice, and best practices for rendering applications with Microsoft DirectX Raytracing (DXR) How to implement high-performance graphics for interactive visualizations, games, simulations, and more Who this book is for: Developers who are looking to leverage the latest APIs and GPU technology for real-time rendering and ray tracing Students looking to learn about best practices in these areas Enthusiasts who want to understand and experiment with their new GPU

Scalable exploration of 3D massive models

Programa Oficial de Doutoramento en Tecnoloxías da Información e as Comunicacións. 5032V01[Resumo] Esta tese presenta unha serie técnicas escalables que avanzan o estado da arte da creación e exploración de grandes modelos tridimensionaies. No ámbito da xeración destes modelos, preséntanse métodos para mellorar a adquisición e procesado de escenas reais, grazas a unha implementación eficiente dun sistema out- of- core de xestión de nubes de puntos, e unha nova metodoloxía escalable de fusión de datos de xeometría e cor para adquisicións con oclusións. No ámbito da visualización de grandes conxuntos de datos, que é o núcleo principal desta tese, preséntanse dous novos métodos. O primeiro é unha técnica adaptabile out-of-core que aproveita o hardware de rasterización da GPU e as occlusion queries para crear lotes coherentes de traballo, que serán procesados por kernels de trazado de raios codificados en shaders, permitindo out-of-core ray-tracing con sombreado e iluminación global. O segundo é un método de compresión agresivo que aproveita a redundancia xeométrica que se adoita atopar en grandes modelos 3D para comprimir os datos de forma que caiban, nun formato totalmente renderizable, na memoria da GPU. O método está deseñado para representacións voxelizadas de escenas 3D, que son amplamente utilizadas para diversos cálculos como para acelerar as consultas de visibilidade na GPU. A compresión lógrase fusionando subárbores idénticas a través dunha transformación de similitude, e aproveitando a distribución non homoxénea de referencias a nodos compartidos para almacenar punteiros aos nodos fillo, e utilizando unha codificación de bits variable. A capacidade e o rendemento de todos os métodos avalíanse utilizando diversos casos de uso do mundo real de diversos ámbitos e sectores, incluídos o patrimonio cultural, a enxeñería e os videoxogos.[Resumen] En esta tesis se presentan una serie técnicas escalables que avanzan el estado del arte de la creación y exploración de grandes modelos tridimensionales. En el ámbito de la generación de estos modelos, se presentan métodos para mejorar la adquisición y procesado de escenas reales, gracias a una implementación eficiente de un sistema out-of-core de gestión de nubes de puntos, y una nueva metodología escalable de fusión de datos de geometría y color para adquisiciones con oclusiones. Para la visualización de grandes conjuntos de datos, que constituye el núcleo principal de esta tesis, se presentan dos nuevos métodos. El primero de ellos es una técnica adaptable out-of-core que aprovecha el hardware de rasterización de la GPU y las occlusion queries, para crear lotes coherentes de trabajo, que serán procesados por kernels de trazado de rayos codificados en shaders, permitiendo renders out-of-core avanzados con sombreado e iluminación global. El segundo es un método de compresión agresivo, que aprovecha la redundancia geométrica que se suele encontrar en grandes modelos 3D para comprimir los datos de forma que quepan, en un formato totalmente renderizable, en la memoria de la GPU. El método está diseñado para representaciones voxelizadas de escenas 3D, que son ampliamente utilizadas para diversos cálculos como la aceleración las consultas de visibilidad en la GPU o el trazado de sombras. La compresión se logra fusionando subárboles idénticos a través de una transformación de similitud, y aprovechando la distribución no homogénea de referencias a nodos compartidos para almacenar punteros a los nodos hijo, utilizando una codificación de bits variable. La capacidad y el rendimiento de todos los métodos se evalúan utilizando diversos casos de uso del mundo real de diversos ámbitos y sectores, incluidos el patrimonio cultural, la ingeniería y los videojuegos.[Abstract] This thesis introduces scalable techniques that advance the state-of-the-art in massive model creation and exploration. Concerning model creation, we present methods for improving reality-based scene acquisition and processing, introducing an efficient implementation of scalable out-of-core point clouds and a data-fusion approach for creating detailed colored models from cluttered scene acquisitions. The core of this thesis concerns enabling technology for the exploration of general large datasets. Two novel solutions are introduced. The first is an adaptive out-of-core technique exploiting the GPU rasterization pipeline and hardware occlusion queries in order to create coherent batches of work for localized shader-based ray tracing kernels, opening the door to out-of-core ray tracing with shadowing and global illumination. The second is an aggressive compression method that exploits redundancy in large models to compress data so that it fits, in fully renderable format, in GPU memory. The method is targeted to voxelized representations of 3D scenes, which are widely used to accelerate visibility queries on the GPU. Compression is achieved by merging subtrees that are identical through a similarity transform and by exploiting the skewed distribution of references to shared nodes to store child pointers using a variable bitrate encoding The capability and performance of all methods are evaluated on many very massive real-world scenes from several domains, including cultural heritage, engineering, and gaming

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

Doctor of Philosophy in Computer Science

dissertationRay tracing is becoming more widely adopted in offline rendering systems due to its natural support for high quality lighting. Since quality is also a concern in most real time systems, we believe ray tracing would be a welcome change in the real time world, but is avoided due to insufficient performance. Since power consumption is one of the primary factors limiting the increase of processor performance, it must be addressed as a foremost concern in any future ray tracing system designs. This will require cooperating advances in both algorithms and architecture. In this dissertation I study ray tracing system designs from a data movement perspective, targeting the various memory resources that are the primary consumer of power on a modern processor. The result is high performance, low energy ray tracing architectures

Hardware Accelerators for Animated Ray Tracing

Future graphics processors are likely to incorporate hardware accelerators for real-time ray tracing, in order to render increasingly complex lighting effects in interactive applications. However, ray tracing poses difficulties when drawing scenes with dynamic content, such as animated characters and objects. In dynamic scenes, the spatial datastructures used to accelerate ray tracing are invalidated on each animation frame, and need to be rapidly updated. Tree update is a complex subtask in its own right, and becomes highly expensive in complex scenes. Both ray tracing and tree update are highly memory-intensive tasks, and rendering systems are increasingly bandwidth-limited, so research on accelerator hardware has focused on architectural techniques to optimize away off-chip memory traffic. Dynamic scene support is further complicated by the recent introduction of compressed trees, which use low-precision numbers for storage and computation. Such compression reduces both the arithmetic and memory bandwidth cost of ray tracing, but adds to the complexity of tree update.This thesis proposes methods to cope with dynamic scenes in hardware-accelerated ray tracing, with focus on reducing traffic to external memory. Firstly, a hardware architecture is designed for linear bounding volume hierarchy construction, an algorithm which is a basic building block in most state-of-the-art software tree builders. The algorithm is rearranged into a streaming form which reduces traffic to one-third of software implementations of the same algorithm. Secondly, an algorithm is proposed for compressing bounding volume hierarchies in a streaming manner as they are output from a hardware builder, instead of performing compression as a postprocessing pass. As a result, with the proposed method, compression reduces the overall cost of tree update rather than increasing it. The last main contribution of this thesis is an evaluation of shallow bounding volume hierarchies, common in software ray tracing, for use in hardware pipelines. These are found to be more energy-efficient than binary hierarchies. The results in this thesis both confirm that dynamic scene support may become a bottleneck in real time ray tracing, and add to the state of the art on tree update in terms of energy-efficiency, as well as the complexity of scenes that can be handled in real time on resource-constrained platforms

Visibility computation through image generalization

This dissertation introduces the image generalization paradigm for computing visibility. The paradigm is based on the observation that an image is a powerful tool for computing visibility. An image can be rendered efficiently with the support of graphics hardware and each of the millions of pixels in the image reports a visible geometric primitive. However, the visibility solution computed by a conventional image is far from complete. A conventional image has a uniform sampling rate which can miss visible geometric primitives with a small screen footprint. A conventional image can only find geometric primitives to which there is direct line of sight from the center of projection (i.e. the eye) of the image; therefore, a conventional image cannot compute the set of geometric primitives that become visible as the viewpoint translates, or as time changes in a dynamic dataset. Finally, like any sample-based representation, a conventional image can only confirm that a geometric primitive is visible, but it cannot confirm that a geometric primitive is hidden, as that would require an infinite number of samples to confirm that the primitive is hidden at all of its points. ^ The image generalization paradigm overcomes the visibility computation limitations of conventional images. The paradigm has three elements. (1) Sampling pattern generalization entails adding sampling locations to the image plane where needed to find visible geometric primitives with a small footprint. (2) Visibility sample generalization entails replacing the conventional scalar visibility sample with a higher dimensional sample that records all geometric primitives visible at a sampling location as the viewpoint translates or as time changes in a dynamic dataset; the higher-dimensional visibility sample is computed exactly, by solving visibility event equations, and not through sampling. Another form of visibility sample generalization is to enhance a sample with its trajectory as the geometric primitive it samples moves in a dynamic dataset. (3) Ray geometry generalization redefines a camera ray as the set of 3D points that project at a given image location; this generalization supports rays that are not straight lines, and enables designing cameras with non-linear rays that circumvent occluders to gather samples not visible from a reference viewpoint. ^ The image generalization paradigm has been used to develop visibility algorithms for a variety of datasets, of visibility parameter domains, and of performance-accuracy tradeoff requirements. These include an aggressive from-point visibility algorithm that guarantees finding all geometric primitives with a visible fragment, no matter how small primitive\u27s image footprint, an efficient and robust exact from-point visibility algorithm that iterates between a sample-based and a continuous visibility analysis of the image plane to quickly converge to the exact solution, a from-rectangle visibility algorithm that uses 2D visibility samples to compute a visible set that is exact under viewpoint translation, a flexible pinhole camera that enables local modulations of the sampling rate over the image plane according to an input importance map, an animated depth image that not only stores color and depth per pixel but also a compact representation of pixel sample trajectories, and a curved ray camera that integrates seamlessly multiple viewpoints into a multiperspective image without the viewpoint transition distortion artifacts of prior art methods

Efficient geometric sound propagation using visibility culling

Simulating propagation of sound can improve the sense of realism in interactive applications such as video games and can lead to better designs in engineering applications such as architectural acoustics. In this thesis, we present geometric sound propagation techniques which are faster than prior methods and map well to upcoming parallel multi-core CPUs. We model specular reflections by using the image-source method and model finite-edge diffraction by using the well-known Biot-Tolstoy-Medwin (BTM) model. We accelerate the computation of specular reflections by applying novel visibility algorithms, FastV and AD-Frustum, which compute visibility from a point. We accelerate finite-edge diffraction modeling by applying a novel visibility algorithm which computes visibility from a region. Our visibility algorithms are based on frustum tracing and exploit recent advances in fast ray-hierarchy intersections, data-parallel computations, and scalable, multi-core algorithms. The AD-Frustum algorithm adapts its computation to the scene complexity and allows small errors in computing specular reflection paths for higher computational efficiency. FastV and our visibility algorithm from a region are general, object-space, conservative visibility algorithms that together significantly reduce the number of image sources compared to other techniques while preserving the same accuracy. Our geometric propagation algorithms are an order of magnitude faster than prior approaches for modeling specular reflections and two to ten times faster for modeling finite-edge diffraction. Our algorithms are interactive, scale almost linearly on multi-core CPUs, and can handle large, complex, and dynamic scenes. We also compare the accuracy of our sound propagation algorithms with other methods. Once sound propagation is performed, it is desirable to listen to the propagated sound in interactive and engineering applications. We can generate smooth, artifact-free output audio signals by applying efficient audio-processing algorithms. We also present the first efficient audio-processing algorithm for scenarios with simultaneously moving source and moving receiver (MS-MR) which incurs less than 25% overhead compared to static source and moving receiver (SS-MR) or moving source and static receiver (MS-SR) scenario

Dynamic task scheduling and binding for many-core systems through stream rewriting

This thesis proposes a novel model of computation, called stream rewriting, for the specification and implementation of highly concurrent applications. Basically, the active tasks of an application and their dependencies are encoded as a token stream, which is iteratively modified by a set of rewriting rules at runtime. In order to estimate the performance and scalability of stream rewriting, a large number of experiments have been evaluated on many-core systems and the task management has been implemented in software and hardware.In dieser Dissertation wurde Stream Rewriting als eine neue Methode entwickelt, um Anwendungen mit einer großen Anzahl von dynamischen Tasks zu beschreiben und effizient zur Laufzeit verwalten zu können. Dabei werden die aktiven Tasks in einem Datenstrom verpackt, der zur Laufzeit durch wiederholtes Suchen und Ersetzen umgeschrieben wird. Um die Performance und Skalierbarkeit zu bestimmen, wurde eine Vielzahl von Experimenten mit Many-Core-Systemen durchgeführt und die Verwaltung von Tasks über Stream Rewriting in Software und Hardware implementiert