Visually pleasing real-time global illumination rendering for fully-dynamic scenes

Global illumination (GI) rendering plays a crucial role in the photo-realistic rendering of virtual scenes. With the rapid development of graphics hardware, GI has become increasingly attractive even for real-time applications nowadays. However, the computation of physically-correct global illumination is time-consuming and cannot achieve real-time, or even interactive performance. Although the realtime GI is possible using a solution based on precomputation, such a solution cannot deal with fully-dynamic scenes. This dissertation focuses on solving these problems by introducing visually pleasing real-time global illumination rendering for fully-dynamic scenes. To this end, we develop a set of novel algorithms and techniques for rendering global illumination effects using the graphics hardware. All these algorithms not only result in real-time or interactive performance, but also generate comparable quality to the previous works in off-line rendering. First, we present a novel implicit visibility technique to circumvent expensive visibility queries in hierarchical radiosity by evaluating the visibility implicitly. Thereafter, we focus on rendering visually plausible soft shadows, which is the most important GI effect caused by the visibility determination. Based on the pre-filtering shadowmapping theory, wesuccessively propose two real-time soft shadow mapping methods: "convolution soft shadow mapping" (CSSM) and "variance soft shadow mapping" (VSSM). Furthermore, we successfully apply our CSSM method in computing the shadow effects for indirect lighting. Finally, to explore the GI rendering in participating media, we investigate a novel technique to interactively render volume caustics in the single-scattering participating media.Das Rendern globaler Beleuchtung ist für die fotorealistische Darstellung virtueller Szenen von entscheidender Bedeutung. Dank der rapiden Entwicklung der Grafik-Hardware wird die globale Beleuchtung heutzutage sogar für Echtzeitanwendungen immer attraktiver. Trotz allem ist die Berechnung physikalisch korrekter globaler Beleuchtung zeitintensiv und interaktive Laufzeiten können mit "standard Hardware" noch nicht erzielt werden. Obwohl das Rendering auf der Grundlage von Vorberechnungen in Echtzeit möglich ist, kann ein solcher Ansatz nicht auf voll-dynamische Szenen angewendet werden. Diese Dissertation zielt darauf ab, das Problem der globalen Beleuchtungsberechnung durch Einführung von neuen Techniken für voll-dynamische Szenen in Echtzeit zu lösen. Dazu stellen wir eine Reihe neuer Algorithmen vor, die die Effekte der globaler Beleuchtung auf der Grafik-Hardware berechnen. All diese Algorithmen erzielen nicht nur Echtzeit bzw. interaktive Laufzeiten sondern liefern auch eine Qualität, die mit bisherigen offline Methoden vergleichbar ist. Zunächst präsentieren wir eine neue Technik zur Berechnung impliziter Sichtbarkeit, die aufwändige Sichbarkeitstests in hierarchischen Radiosity-Datenstrukturen vermeidet. Anschliessend stellen wir eine Methode vor, die weiche Schatten, ein wichtiger Effekt für die globale Beleuchtung, in Echtzeit berechnet. Auf der Grundlage der Theorie über vorgefilterten Schattenwurf, zeigen wir nacheinander zwei Echtzeitmethoden zur Berechnung weicher Schattenwürfe: "Convolution Soft Shadow Mapping" (CSSM) und "Variance Soft Shadow Mapping" (VSSM). Darüber hinaus wenden wir unsere CSSM-Methode auch erfolgreich auf den Schatteneffekt in der indirekten Beleuchtung an. Abschliessend präsentieren wir eine neue Methode zum interaktiven Rendern von Volumen-Kaustiken in einfach streuenden, halbtransparenten Medien

Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data

This paper presents a new method for shadow removal using unpaired data, enabling us to avoid tedious annotations and obtain more diverse training samples. However, directly employing adversarial learning and cycle-consistency constraints is insufficient to learn the underlying relationship between the shadow and shadow-free domains, since the mapping between shadow and shadow-free images is not simply one-to-one. To address the problem, we formulate Mask-ShadowGAN, a new deep framework that automatically learns to produce a shadow mask from the input shadow image and then takes the mask to guide the shadow generation via re-formulated cycle-consistency constraints. Particularly, the framework simultaneously learns to produce shadow masks and learns to remove shadows, to maximize the overall performance. Also, we prepared an unpaired dataset for shadow removal and demonstrated the effectiveness of Mask-ShadowGAN on various experiments, even it was trained on unpaired data.Comment: Accepted to ICCV 201

Rapid computation of far-field statistics for random obstacle scattering

In this article, we consider the numerical approximation of far-field statistics for acoustic scattering problems in the case of random obstacles. In particular, we consider the computation of the expected far-field pattern and the expected scattered wave away from the scatterer as well as the computation of the corresponding variances. To that end, we introduce an artificial interface, which almost surely contains all realizations of the random scatterer. At this interface, we directly approximate the second order statistics, i.e., the expectation and the variance, of the Cauchy data by means of boundary integral equations. From these quantities, we are able to rapidly evaluate statistics of the scattered wave everywhere in the exterior domain, including the expectation and the variance of the far-field. By employing a low-rank approximation of the Cauchy data's two-point correlation function, we drastically reduce the cost of the computation of the scattered wave's variance. Numerical results are provided in order to demonstrate the feasibility of the proposed approach

Master of Science in Computing

thesisThis document introduces the Soft Shadow Mip-Maps technique, which consists of three methods for overcoming the fundamental limitations of filtering-oriented soft shadows. Filtering-oriented soft shadowing techniques filter shadow maps with varying filter sizes determined by desired penumbra widths. Different varieties of this approach have been commonly applied in interactive and real-time applications. Nonetheless, they share some fundamental limitations. First, soft shadow filter size is not always guaranteed to be the correct size for producing the right penumbra width based on the light source size. Second, filtering with large kernels for soft shadows requires a large number of samples, thereby increasing the cost of filtering. Stochastic approximations for filtering introduce noise and prefiltering leads to inaccuracies. Finally, calculating shadows based on a single blocker estimation can produce significantly inaccurate penumbra widths when the shadow penumbras of different blockers overlap. We discuss three methods to overcome these limitations. First, we introduce a method for computing the soft shadow filter size for a receiver with a blocker distance. Then, we present a filtering scheme based on shadow mip-maps. Mipmap-based filtering uses shadow mip-maps to efficiently generate soft shadows using a constant size filter kernel for each layer, and linear interpolation between layers. Finally, we introduce an improved blocker estimation approach. With the improved blocker estimaiton, we explore the shadow contribution of every blocker by calculating the light occluded by potential blockers. Hence, the calculated penumbra areas correspond to the blockers correctly. Finally, we discuss how to select filter kernels for filtering. These approaches successively solve issues regarding shadow penumbra width calculation apparent in prior techniques. Our result shows that we can produce correct penumbra widths, as evident in our comparisons to ray-traced soft shadows. Nonetheless, the Soft Shadow Mip-Maps technique suffers from light bleeding issues. This is because our method only calculates shadows using the geometry that is available in the shadow depth map. Therefore, the occluded geometry is not taken into consideration, which leads to light bleeding. Another limitation of our method is that using lower resolution shadow mip-map layers limits the resolution of the shadow placement. As a result, when a blocker moves slowly, its shadow follows it with discrete steps, the size of which is determined by the corresponding mip-map layer resolution

Shadow mapping algorithms: Applications and limitations

This study provides an overview of popular and famous algorithms and techniques in shadow maps generation.Well- known techniques in shadow maps generation is described detail, along with a discussion of the advantages and drawbacks of each. Basic ideas, improvements and future works of the techniques are also comprehensively summarized and analyzed in depth. Often, programmers have difficulty selecting an appropriate shadow generation algorithm that is specific to their purpose. We have classified and systemized these techniques. The main goal of this paper is to provide researchers with background on a variety of shadow mapping techniques so as make it easier for them to choose the method best suited to their aims. It is al-so hoped that our analysis will help researchers find solutions to the shortcomings of each technique. © 2015 NSP Natural Sciences Publishing Co

AROMA: Automatic Generation of Radio Maps for Localization Systems

WLAN localization has become an active research field recently. Due to the wide WLAN deployment, WLAN localization provides ubiquitous coverage and adds to the value of the wireless network by providing the location of its users without using any additional hardware. However, WLAN localization systems usually require constructing a radio map, which is a major barrier of WLAN localization systems' deployment. The radio map stores information about the signal strength from different signal strength streams at selected locations in the site of interest. Typical construction of a radio map involves measurements and calibrations making it a tedious and time-consuming operation. In this paper, we present the AROMA system that automatically constructs accurate active and passive radio maps for both device-based and device-free WLAN localization systems. AROMA has three main goals: high accuracy, low computational requirements, and minimum user overhead. To achieve high accuracy, AROMA uses 3D ray tracing enhanced with the uniform theory of diffraction (UTD) to model the electric field behavior and the human shadowing effect. AROMA also automates a number of routine tasks, such as importing building models and automatic sampling of the area of interest, to reduce the user's overhead. Finally, AROMA uses a number of optimization techniques to reduce the computational requirements. We present our system architecture and describe the details of its different components that allow AROMA to achieve its goals. We evaluate AROMA in two different testbeds. Our experiments show that the predicted signal strength differs from the measurements by a maximum average absolute error of 3.18 dBm achieving a maximum localization error of 2.44m for both the device-based and device-free cases.Comment: 14 pages, 17 figure