1,989 research outputs found
Efficient and High-Quality Rendering of Higher-Order Geometric Data Representations
Computer-Aided Design (CAD) bezeichnet den Entwurf industrieller Produkte mit Hilfe von virtuellen 3D Modellen. Ein CAD-Modell besteht aus parametrischen Kurven und Flächen, in den meisten Fällen non-uniform rational B-Splines (NURBS). Diese mathematische Beschreibung wird ebenfalls zur Analyse, Optimierung und Präsentation des Modells verwendet. In jeder dieser Entwicklungsphasen wird eine unterschiedliche visuelle Darstellung benötigt, um den entsprechenden Nutzern ein geeignetes Feedback zu geben. Designer bevorzugen beispielsweise illustrative oder realistische Darstellungen, Ingenieure benötigen eine verständliche Visualisierung der Simulationsergebnisse, während eine immersive 3D Darstellung bei einer Benutzbarkeitsanalyse oder der Designauswahl hilfreich sein kann. Die interaktive Darstellung von NURBS-Modellen und -Simulationsdaten ist jedoch aufgrund des hohen Rechenaufwandes und der eingeschränkten Hardwareunterstützung eine große Herausforderung.
Diese Arbeit stellt vier neuartige Verfahren vor, welche sich mit der interaktiven Darstellung von NURBS-Modellen und Simulationensdaten befassen. Die vorgestellten Algorithmen nutzen neue Fähigkeiten aktueller Grafikkarten aus, um den Stand der Technik bezüglich Qualität, Effizienz und Darstellungsgeschwindigkeit zu verbessern. Zwei dieser Verfahren befassen sich mit der direkten Darstellung der parametrischen Beschreibung ohne Approximationen oder zeitaufwändige Vorberechnungen. Die dabei vorgestellten Datenstrukturen und Algorithmen ermöglichen die effiziente Unterteilung, Klassifizierung, Tessellierung und Darstellung getrimmter NURBS-Flächen und einen interaktiven Ray-Casting-Algorithmus für die Isoflächenvisualisierung von NURBSbasierten isogeometrischen Analysen. Die weiteren zwei Verfahren beschreiben zum einen das vielseitige Konzept der programmierbaren Transparenz für illustrative und verständliche Visualisierungen tiefenkomplexer CAD-Modelle und zum anderen eine neue hybride Methode zur Reprojektion halbtransparenter und undurchsichtiger Bildinformation für die Beschleunigung der Erzeugung von stereoskopischen Bildpaaren. Die beiden letztgenannten Ansätze basieren auf rasterisierter Geometrie und sind somit ebenfalls für normale Dreiecksmodelle anwendbar, wodurch die Arbeiten auch einen wichtigen Beitrag in den Bereichen der Computergrafik und der virtuellen Realität darstellen.
Die Auswertung der Arbeit wurde mit großen, realen NURBS-Datensätzen durchgeführt. Die Resultate zeigen, dass die direkte Darstellung auf Grundlage der parametrischen Beschreibung mit interaktiven Bildwiederholraten und in subpixelgenauer Qualität möglich ist. Die Einführung programmierbarer Transparenz ermöglicht zudem die Umsetzung kollaborativer 3D Interaktionstechniken für die Exploration der Modelle in virtuellenUmgebungen sowie illustrative und verständliche Visualisierungen tiefenkomplexer CAD-Modelle. Die Erzeugung stereoskopischer Bildpaare für die interaktive Visualisierung auf 3D Displays konnte beschleunigt werden. Diese messbare Verbesserung wurde zudem im Rahmen einer Nutzerstudie als wahrnehmbar und vorteilhaft befunden.In computer-aided design (CAD), industrial products are designed using a virtual 3D model. A CAD model typically consists of curves and surfaces in a parametric representation, in most cases, non-uniform rational B-splines (NURBS). The same representation is also used for the analysis, optimization and presentation of the model. In each phase of this process, different visualizations are required to provide an appropriate user feedback. Designers work with illustrative and realistic renderings, engineers need a
comprehensible visualization of the simulation results, and usability studies or product presentations benefit from using a 3D display. However, the interactive visualization of NURBS models and corresponding physical simulations is a challenging task because of the computational complexity and the limited graphics hardware support.
This thesis proposes four novel rendering approaches that improve the interactive visualization of CAD models and their analysis. The presented algorithms exploit latest graphics hardware capabilities to advance the state-of-the-art in terms of quality, efficiency and performance. In particular, two approaches describe the direct rendering of the parametric representation without precomputed approximations and timeconsuming pre-processing steps. New data structures and algorithms are presented for the efficient partition, classification, tessellation, and rendering of trimmed NURBS surfaces as well as the first direct isosurface ray-casting approach for NURBS-based isogeometric analysis. The other two approaches introduce the versatile concept of programmable order-independent semi-transparency for the illustrative and comprehensible visualization of depth-complex CAD models, and a novel method for the hybrid reprojection of opaque and semi-transparent image information to accelerate stereoscopic rendering. Both approaches are also applicable to standard polygonal geometry which contributes to the computer graphics and virtual reality research communities.
The evaluation is based on real-world NURBS-based models and simulation data. The results show that rendering can be performed directly on the underlying parametric representation with interactive frame rates and subpixel-precise image results. The computational costs of additional visualization effects, such as semi-transparency and stereoscopic rendering, are reduced to maintain interactive frame rates. The benefit of this performance gain was confirmed by quantitative measurements and a pilot user study
Feed-forward volume rendering algorithm for moderately parallel MIMD machines
Algorithms for direct volume rendering on parallel and vector processors are investigated. Volumes are transformed efficiently on parallel processors by dividing the data into slices and beams of voxels. Equal sized sets of slices along one axis are distributed to processors. Parallelism is achieved at two levels. Because each slice can be transformed independently of others, processors transform their assigned slices with no communication, thus providing maximum possible parallelism at the first level. Within each slice, consecutive beams are incrementally transformed using coherency in the transformation computation. Also, coherency across slices can be exploited to further enhance performance. This coherency yields the second level of parallelism through the use of the vector processing or pipelining. Other ongoing efforts include investigations into image reconstruction techniques, load balancing strategies, and improving performance
Mobile graphics: SIGGRAPH Asia 2017 course
Peer ReviewedPostprint (published version
Fragment-History Volumes
Hardware-based triangle rasterization is still the prevalent method for
generating images at real-time interactive frame rates. With the availability
of a programmable graphics pipeline a large variety of techniques are supported
for evaluating lighting and material properties of fragments. However, these
techniques are usually restricted to evaluating local lighting and material
effects. In addition, view-point changes require the complete processing of
scene data to generate appropriate images. Reusing already rendered data in the
frame buffer for a given view point by warping for a new viewpoint increases
navigation fidelity at the expense of introducing artifacts for fragments
previously hidden from the viewer.
We present fragment-history volumes (FHV), a rendering technique based on a
sparse, discretized representation of a 3d scene that emerges from recording
all fragments that pass the rasterization stage in the graphics pipeline. These
fragments are stored into per-pixel or per-octant lists for further processing;
essentially creating an A-buffer. FHVs using per-octant fragment lists are view
independent and allow fast resampling for image generation as well as for using
more sophisticated approaches to evaluate material and lighting properties,
eventually enabling global-illumination evaluation in the standard graphics
pipeline available on current hardware.
We show how FHVs are stored on the GPU in several ways, how they are created,
and how they can be used for image generation at high rates. We discuss results
for different usage scenarios, variations of the technique, and some
limitations
Deep-learning the Latent Space of Light Transport
We suggest a method to directly deep‐learn light transport, i. e., the mapping from a 3D geometry‐illumination‐material configuration to a shaded 2D image. While many previous learning methods have employed 2D convolutional neural networks applied to images, we show for the first time that light transport can be learned directly in 3D. The benefit of 3D over 2D is, that the former can also correctly capture illumination effects related to occluded and/or semi‐transparent geometry. To learn 3D light transport, we represent the 3D scene as an unstructured 3D point cloud, which is later, during rendering, projected to the 2D output image. Thus, we suggest a two‐stage operator comprising a 3D network that first transforms the point cloud into a latent representation, which is later on projected to the 2D output image using a dedicated 3D‐2D network in a second step. We will show that our approach results in improved quality in terms of temporal coherence while retaining most of the computational efficiency of common 2D methods. As a consequence, the proposed two stage‐operator serves as a valuable extension to modern deferred shading approaches
Training and Predicting Visual Error for Real-Time Applications
Visual error metrics play a fundamental role in the quantification of
perceived image similarity. Most recently, use cases for them in real-time
applications have emerged, such as content-adaptive shading and shading reuse
to increase performance and improve efficiency. A wide range of different
metrics has been established, with the most sophisticated being capable of
capturing the perceptual characteristics of the human visual system. However,
their complexity, computational expense, and reliance on reference images to
compare against prevent their generalized use in real-time, restricting such
applications to using only the simplest available metrics. In this work, we
explore the abilities of convolutional neural networks to predict a variety of
visual metrics without requiring either reference or rendered images.
Specifically, we train and deploy a neural network to estimate the visual error
resulting from reusing shading or using reduced shading rates. The resulting
models account for 70%-90% of the variance while achieving up to an order of
magnitude faster computation times. Our solution combines image-space
information that is readily available in most state-of-the-art deferred shading
pipelines with reprojection from previous frames to enable an adequate estimate
of visual errors, even in previously unseen regions. We describe a suitable
convolutional network architecture and considerations for data preparation for
training. We demonstrate the capability of our network to predict complex error
metrics at interactive rates in a real-time application that implements
content-adaptive shading in a deferred pipeline. Depending on the portion of
unseen image regions, our approach can achieve up to performance
compared to state-of-the-art methods.Comment: Published at Proceedings of the ACM in Computer Graphics and
Interactive Techniques. 14 Pages, 16 Figures, 3 Tables. For paper website and
higher quality figures, see https://jaliborc.github.io/rt-percept
High-Level GPU Programming: Domain-Specific Optimization and Inference
When writing computer software one is often forced to balance the need for high run-time performance with high programmer productivity. By using a high-level language it is often possible to cut development times, but this typically comes at the cost of reduced run-time performance. Using a lower-level language, programs can be made very efficient but at the cost of increased development time. Real-time computer graphics is an area where there are very high demands on both performance and visual quality. Typically, large portions of such applications are written in lower-level languages and also rely on dedicated hardware, in the form of programmable graphics processing units (GPUs), for handling computationally demanding rendering algorithms. These GPUs are parallel stream processors, specialized towards computer graphics, that have computational performance more than a magnitude higher than corresponding CPUs. This has revolutionized computer graphics and also led to GPUs being used to solve more general numerical problems, such as fluid and physics simulation, protein folding, image processing, and databases. Unfortunately, the highly specialized nature of GPUs has also made them difficult to program. In this dissertation we show that GPUs can be programmed at a higher level, while maintaining performance, compared to current lower-level languages. By constructing a domain-specific language (DSL), which provides appropriate domain-specific abstractions and user-annotations, it is possible to write programs in a more abstract and modular manner. Using knowledge of the domain it is possible for the DSL compiler to generate very efficient code. We show that, by experiment, the performance of our DSLs is equal to that of GPU programs written by hand using current low-level languages. Also, control over the trade-offs between visual quality and performance is retained. In the papers included in this dissertation, we present domain-specific languages targeted at numerical processing and computer graphics, respectively. These DSL have been implemented as embedded languages in Python, a dynamic programming language that provide a rich set of high-level features. In this dissertation we show how these features can be used to facilitate the construction of embedded languages
Playing for Data: Ground Truth from Computer Games
Recent progress in computer vision has been driven by high-capacity models
trained on large datasets. Unfortunately, creating large datasets with
pixel-level labels has been extremely costly due to the amount of human effort
required. In this paper, we present an approach to rapidly creating
pixel-accurate semantic label maps for images extracted from modern computer
games. Although the source code and the internal operation of commercial games
are inaccessible, we show that associations between image patches can be
reconstructed from the communication between the game and the graphics
hardware. This enables rapid propagation of semantic labels within and across
images synthesized by the game, with no access to the source code or the
content. We validate the presented approach by producing dense pixel-level
semantic annotations for 25 thousand images synthesized by a photorealistic
open-world computer game. Experiments on semantic segmentation datasets show
that using the acquired data to supplement real-world images significantly
increases accuracy and that the acquired data enables reducing the amount of
hand-labeled real-world data: models trained with game data and just 1/3 of the
CamVid training set outperform models trained on the complete CamVid training
set.Comment: Accepted to the 14th European Conference on Computer Vision (ECCV
2016
Deep Shading: Convolutional Neural Networks for Screen-Space Shading
In computer vision, Convolutional Neural Networks (CNNs) have recently achieved new levels of performance for several inverse problems where RGB pixel appearance is mapped to attributes such as positions, normals or reflectance. In computer graphics, screen-space shading has recently increased the visual quality in interactive image synthesis, where per-pixel attributes such as positions, normals or reflectance of a virtual 3D scene are converted into RGB pixel appearance, enabling effects like ambient occlusion, indirect light, scattering, depth-of-field, motion blur, or anti-aliasing. In this paper we consider the diagonal problem: synthesizing appearance from given per-pixel attributes using a CNN. The resulting Deep Shading simulates all screen-space effects as well as arbitrary combinations thereof at competitive quality and speed while not being programmed by human experts but learned from example images
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