Efficient Object-Based Hierarchical Radiosity Methods

The efficient generation of photorealistic images is one of the main subjects in the field of computer graphics. In contrast to simple image generation which is directly supported by standard 3D graphics hardware, photorealistic image synthesis strongly adheres to the physics describing the flow of light in a given environment. By simulating the energy flow in a 3D scene global effects like shadows and inter-reflections can be rendered accurately. The hierarchical radiosity method is one way of computing the global illumination in a scene. Due to its limitation to purely diffuse surfaces solutions computed by this method are view independent and can be examined in real-time walkthroughs. Additionally, the physically based algorithm makes it well suited for lighting design and architectural visualization. The focus of this thesis is the application of object-oriented methods to the radiosity problem. By consequently keeping and using object information throughout all stages of the algorithms several contributions to the field of radiosity rendering could be made. By introducing a new meshing scheme, it is shown how curved objects can be treated efficiently by hierarchical radiosity algorithms. Using the same paradigm the radiosity computation can be distributed in a network of computers. A parallel implementation is presented that minimizes communication costs while obtaining an efficient speedup. Radiosity solutions for very large scenes became possible by the use of clustering algorithms. Groups of objects are combined to clusters to simulate the energy exchange on a higher abstraction level. It is shown how the clustering technique can be improved without loss in image quality by applying the same data-structure for both, the visibility computations and the efficient radiosity simulation.Eines der Schwerpunktthemen in der Computergraphik ist die effiziente Erzeugung von fotorealistischen Bildern. Im Gegensatz zur einfachen Bilderzeugung, die bereits durch gaengige 3D-Grafikhardware unterstuetzt wird, gehorcht die fotorealistische Bildsynthese physikalischen Gesetzen, die die Lichtausbreitung innerhalb einer bestimmten Umgebung beschreiben. Durch die Simulation der Energieausbreitung in einer dreidimensionalen Szene koennen globale Effekte wie Schatten und mehrfache Reflektionen wirklichkeitstreu dargestellt werden. Die hierarchische Radiositymethode (Hierarchical Radiosity) ist eine Moeglichkeit, um die globale Beleuchtung innerhalb einer Szene zu berechnen. Da diese Methode auf die Verwendung von rein diffus reflektierenden Oberflaechen beschraenkt ist, sind damit errechnete Loesungen blickwinkelunabhaengig und lassen sich in Echtzeit am Bildschirm durchwandern. Zudem ist dieser Algorithmus aufgrund der verwendeten physikalischen Grundlagen sehr gut zur Beleuchtungssimulation und Architekturvisualisierung geeignet. Den Schwerpunkt dieser Doktorarbeit stellt die Anwendung objektbasierter Methoden auf das Radiosityproblem dar. Durch konsequente Ausnutzung von Objektinformationen waehrend aller Berechnungsschritte konnten verschiedene Verbesserungen im Rahmen der hierarchischen Radiositymethode erzielt werden. Gekruemmte Objekte koennen aufgrund eines neuen Flaechenunterteilungsverfahrens nun effizient durch den hierarchischen Radiosityalgorithmus dargestellt werden. Dieses Verfahren ermoeglicht ebenso eine effiziente Parallelisierung des hierarchischen Radiosityalgorithmus. Es wird ein parallele Implementierung vorgestellt, die unter Minimierung der Kommunikationskosten eine effiziente Geschwindigkeitssteigerung erzielt. Radiosityberechnungen fuer sehr grosse Szenen sind nur durch Verwendung sogenannter Clustering-Algorithmen moeglich. Dabei werden Gruppen von Objekten zu Clustern kombiniert um den Energieaustausch zwischen Oberflaechen stellvertretend auf einem hoeheren Abstraktionsniveau durchzufuehren. Durch Verwendung derselben Datenstruktur fuer Sichtbarkeitsberechnungen und fuer die Steuerung der Radiositysimulation wird gezeigt, wie das Clusteringverfahren ohne Qualitaetsverluste verbessert werden kann

Camera based Display Image Quality Assessment

This thesis presents the outcomes of research carried out by the PhD candidate Ping Zhao during 2012 to 2015 in Gjøvik University College. The underlying research was a part of the HyPerCept project, in the program of Strategic Projects for University Colleges, which was funded by The Research Council of Norway. The research was engaged under the supervision of Professor Jon Yngve Hardeberg and co-supervision of Associate Professor Marius Pedersen, from The Norwegian Colour and Visual Computing Laboratory, in the Faculty of Computer Science and Media Technology of Gjøvik University College; as well as the co-supervision of Associate Professor Jean-Baptiste Thomas, from The Laboratoire Electronique, Informatique et Image, in the Faculty of Computer Science of Universit´e de Bourgogne. The main goal of this research was to develop a fast and an inexpensive camera based display image quality assessment framework. Due to the limited time frame, we decided to focus only on projection displays with static images displayed on them. However, the proposed methods were not limited to projection displays, and they were expected to work with other types of displays, such as desktop monitors, laptop screens, smart phone screens, etc., with limited modifications. The primary contributions from this research can be summarized as follows: 1. We proposed a camera based display image quality assessment framework, which was originally designed for projection displays but it can be used for other types of displays with limited modifications. 2. We proposed a method to calibrate the camera in order to eliminate unwanted vignetting artifact, which is mainly introduced by the camera lens. 3. We proposed a method to optimize the camera’s exposure with respect to the measured luminance of incident light, so that after the calibration all camera sensors share a common linear response region. 4. We proposed a marker-less and view-independent method to register one captured image with its original at a sub-pixel level, so that we can incorporate existing full reference image quality metrics without modifying them. 5. We identified spatial uniformity, contrast and sharpness as the most important image quality attributes for projection displays, and we used the proposed framework to evaluate the prediction performance of the state-of-the-art image quality metrics regarding these attributes. The proposed image quality assessment framework is the core contribution of this research. Comparing to conventional image quality assessment approaches, which were largely based on the measurements of colorimeter or spectroradiometer, using camera as the acquisition device has the advantages of quickly recording all displayed pixels in one shot, relatively inexpensive to purchase the instrument. Therefore, the consumption of time and resources for image quality assessment can be largely reduced. We proposed a method to calibrate the camera in order to eliminate unwanted vignetting artifact primarily introduced by the camera lens. We used a hazy sky as a closely uniform light source, and the vignetting mask was generated with respect to the median sensor responses over i only a few rotated shots of the same spot on the sky. We also proposed a method to quickly determine whether all camera sensors were sharing a common linear response region. In order to incorporate existing full reference image quality metrics without modifying them, an accurate registration of pairs of pixels between one captured image and its original is required. We proposed a marker-less and view-independent image registration method to solve this problem. The experimental results proved that the proposed method worked well in the viewing conditions with a low ambient light. We further identified spatial uniformity, contrast and sharpness as the most important image quality attributes for projection displays. Subsequently, we used the developed framework to objectively evaluate the prediction performance of the state-of-art image quality metrics regarding these attributes in a robust manner. In this process, the metrics were benchmarked with respect to the correlations between the prediction results and the perceptual ratings collected from subjective experiments. The analysis of the experimental results indicated that our proposed methods were effective and efficient. Subjective experiment is an essential component for image quality assessment; however it can be time and resource consuming, especially in the cases that additional image distortion levels are required to extend the existing subjective experimental results. For this reason, we investigated the possibility of extending subjective experiments with baseline adjustment method, and we found that the method could work well if appropriate strategies were applied. The underlying strategies referred to the best distortion levels to be included in the baseline, as well as the number of them

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

The velocity dispersion and mass function of the outer halo globular cluster Palomar 4

We obtained precise line-of-sight radial velocities of 23 member stars of the remote halo globular cluster Palomar 4 (Pal 4) using the High Resolution Echelle Spectrograph (HIRES) at the Keck I telescope. We also measured the mass function of the cluster down to a limiting magnitude of V~28 mag using archival HST/WFPC2 imaging. We derived the cluster's surface brightness profile based on the WFPC2 data and on broad-band imaging with the Low-Resolution Imaging Spectrometer (LRIS) at the Keck II telescope. We find a mean cluster velocity of 72.55+/-0.22 km/s and a velocity dispersion of 0.87+/-0.18 km/s. The global mass function of the cluster, in the mass range 0.55<=M<=0.85 M_solar, is shallower than a Kroupa mass function and the cluster is significantly depleted in low-mass stars in its center compared to its outskirts. Since the relaxation time of Pal 4 is of the order of a Hubble time, this points to primordial mass segregation in this cluster. Extrapolating the measured mass function towards lower-mass stars and including the contribution of compact remnants, we derive a total cluster mass of 29800 M_solar. For this mass, the measured velocity dispersion is consistent with the expectations of Newtonian dynamics and below the prediction of Modified Newtonian Dynamics (MOND). Pal 4 adds to the growing body of evidence that the dynamics of star clusters in the outer Galactic halo can hardly be explained by MOND.Comment: 17 pages, accepted for publication in MNRAS; Fig. 8 surface brightness/density data at github.com/matthiasjfrank/pal4_surface_brightnes

Higher-Order Regularization in Computer Vision

At the core of many computer vision models lies the minimization of an objective function consisting of a sum of functions with few arguments. The order of the objective function is defined as the highest number of arguments of any summand. To reduce ambiguity and noise in the solution, regularization terms are included into the objective function, enforcing different properties of the solution. The most commonly used regularization is penalization of boundary length, which requires a second-order objective function. Most of this thesis is devoted to introducing higher-order regularization terms and presenting efficient minimization schemes. One of the topics of the thesis covers a reformulation of a large class of discrete functions into an equivalent form. The reformulation is shown, both in theory and practical experiments, to be advantageous for higher-order regularization models based on curvature and second-order derivatives. Another topic is the parametric max-flow problem. An analysis is given, showing its inherent limitations for large-scale problems which are common in computer vision. The thesis also introduces a segmentation approach for finding thin and elongated structures in 3D volumes. Using a line-graph formulation, it is shown how to efficiently regularize with respect to higher-order differential geometric properties such as curvature and torsion. Furthermore, an efficient optimization approach for a multi-region model is presented which, in addition to standard regularization, is able to enforce geometric constraints such as inclusion or exclusion of different regions. The final part of the thesis deals with dense stereo estimation. A new regularization model is introduced, penalizing the second-order derivatives of a depth or disparity map. Compared to previous second-order approaches to dense stereo estimation, the new regularization model is shown to be more easily optimized