Extraction and Integration of Physical Illumination in Dynamic Augmented Reality Environments

Indiana University-Purdue University Indianapolis (IUPUI)Although current augmented, virtual, and mixed reality (AR/VR/MR) systems are facing advanced and immersive experience in the entertainment industry with countless media forms. Theses systems suffer a lack of correct direct and indirect illumination modeling where the virtual objects render with the same lighting condition as the real environment. Some systems are using baked GI, pre-recorded textures, and light probes that are mostly accomplished offline to compensate for precomputed real-time global illumination (GI). Thus, illumination information can be extracted from the physical scene for interactively rendering the virtual objects into the real world which produces a more realistic final scene in real-time. This work approaches the problem of visual coherence in AR by proposing a system that detects the real-world lighting conditions in dynamic scenes, then uses the extracted illumination information to render the objects added to the scene. The system covers several major components to achieve a more realistic augmented reality outcome. First, the detection of the incident light (direct illumination) from the physical scene with the use of computer vision techniques based on the topological structural analysis of 2D images using a live-feed 360-degree camera instrumented on an AR device that captures the entire radiance map. Also, the physics-based light polarization eliminates or reduces false-positive lights such as white surfaces, reflections, or glare which negatively affect the light detection process. Second, the simulation of the reflected light (indirect illumination) that bounce between the real-world surfaces to be rendered into the virtual objects and reflect their existence in the virtual world. Third, defining the shading characteristic/properties of the virtual object to depict the correct lighting assets with a suitable shadow casting. Fourth, the geometric properties of real-scene including plane detection, 3D surface reconstruction, and simple meshing are incorporated with the virtual scene for more realistic depth interactions between the real and virtual objects. These components are developed methods which assumed to be working simultaneously in real-time for photo-realistic AR. The system is tested with several lighting conditions to evaluate the accuracy of the results based on the error incurred between the real/virtual objects casting shadow and interactions. For system efficiency, the rendering time is compared with previous works and research. Further evaluation of human perception is conducted through a user study. The overall performance of the system is investigated to reduce the cost to a minimum

MOBILE, HYBRID COMPTON/CODED APERTURE IMAGING FOR DETECTION, IDENTIFICATION AND LOCALIZATION OF GAMMA-RAY SOURCES AT STAND-OFF DISTANCES

The Stand-off radiation detection system (SORDS) program is an advanced technology demonstration (ATD) project through the Domestic Nuclear Detection Office (DNDO) with the goal of detection, identification and localization of weak radiological sources in the presence of large dynamic backgrounds. The Raytheon-Tri-Modal Imager (TMI) is a mobile truck-based, hybrid gamma-ray spectroscopic and imaging system able to quickly detect, identify and localize, radiation sources at standoff distances through improved sensitivity provided by multiple detection modes while minimizing the false alarm rate. Reconstruction of gamma-ray sources is performed using a combination of gamma-ray spectroscopy and two imaging modalities; coded aperture and Compton scatter imaging. The TMI consists of 35 NaI crystals (5x5x2 in each), arranged in a random coded aperture CA, followed by 30 position sensitive NaI bars (24x2.5x3 in each) called the DA. The CA array acts as both a coded aperture mask and scattering detector for Compton events. The large-area DA array acts as a collection detector for both Compton scattered events and coded aperture events. In this thesis, the implemented spectroscopic, coded aperture, Compton and hybrid imaging algorithms will be described along with their performance. It will be shown that multiple imaging modalities can be fused to improve detection sensitivity over a broader energy range than any mode alone. Since the TMI is a moving system, peripheral data, such as a GPS and INS must also be incorporated. A method of adapting static imaging algorithms to a moving platform has been developed. Also, algorithms were developed in parallel with detector hardware, through the use of extensive simulations performed with the GEANT4. Simulations have been well validated against measured data. Results of image reconstruction algorithms at various speeds and distances will be presented as well as localization capability. Utilizing imaging information will show signal-to-noise gains over spectroscopic algorithms alone

Research reports: 1990 NASA/ASEE Summer Faculty Fellowship Program

Reports on the research projects performed under the NASA/ASEE Summer Faculty Fellowship Program are presented. The program was conducted by The University of Alabama and MSFC during the period from June 4, 1990 through August 10, 1990. Some of the topics covered include: (1) Space Shuttles; (2) Space Station Freedom; (3) information systems; (4) materials and processes; (4) Space Shuttle main engine; (5) aerospace sciences; (6) mathematical models; (7) mission operations; (8) systems analysis and integration; (9) systems control; (10) structures and dynamics; (11) aerospace safety; and (12) remote sensin

Towards Real-Time Novel View Synthesis Using Visual Hulls

This thesis discusses fast novel view synthesis from multiple images taken from different viewpoints. We propose several new algorithms that take advantage of modern graphics hardware to create novel views. Although different approaches are explored, one geometry representation, the visual hull, is employed throughout our work. First the visual hull plays an auxiliary role and assists in reconstruction of depth maps that are utilized for novel view synthesis. Then we treat the visual hull as the principal geometry representation of scene objects. A hardwareaccelerated approach is presented to reconstruct and render visual hulls directly from a set of silhouette images. The reconstruction is embedded in the rendering process and accomplished with an alpha map trimming technique. We go on by combining this technique with hardware-accelerated CSG reconstruction to improve the rendering quality of visual hulls. Finally, photometric information is exploited to overcome an inherent limitation of the visual hull. All algorithms are implemented on a distributed system. Novel views are generated at interactive or real-time frame rates.In dieser Dissertation werden mehrere Verfahren vorgestellt, mit deren Hilfe neue Ansichten einer Szene aus mehreren Bildströmen errechnet werden können. Die Bildströme werden hierzu aus unterschiedlichen Blickwinkeln auf die Szene aufgezeichnet. Wir schlagen mehrere Algorithmen vor, welche die Funktionen moderner Grafikhardware ausnutzen, um die neuen Ansichten zu errechnen. Obwohl die Verfahren sich methodisch unterscheiden, basieren sie auf der gleichen Geometriedarstellung, der Visual Hull. In der ersten Methode spielt die Visual Hull eine unterstützende Rolle bei der Rekonstruktion von Tiefenbildern, die zur Erzeugung neuer Ansichten verwendet werden. In den nachfolgend vorgestellten Verfahren dient die Visual Hull primär der Repräsentation von Objekten in einer Szene. Eine hardwarebeschleunigte Methode, um Visual Hulls direkt aus mehreren Silhouettenbildern zu rekonstruieren und zu rendern, wird vorgestellt. Das Rekonstruktionsverfahren ist hierbei Bestandteil der Renderingmethode und basiert auf einer Alpha Map Trimming Technik. Ein weiterer Algorithmus verbessert die Qualitaet der gerenderten Visual Hulls, indem das Alpha-Map-basierte Verfahren mit einer hardware-beschleunigten CSG Rekonstruktiontechnik kombiniert wird. Eine vierte Methode nutzt zusaetzlich photometrische Information aus, um eine grundlegende Beschraenkung des Visual-Hull-Ansatzes zu umgehen. Alle Verfahren ermoeglichen die interaktive oder Echtzeit- Erzeugung neuer Ansichten

COBE's search for structure in the Big Bang

The launch of Cosmic Background Explorer (COBE) and the definition of Earth Observing System (EOS) are two of the major events at NASA-Goddard. The three experiments contained in COBE (Differential Microwave Radiometer (DMR), Far Infrared Absolute Spectrophotometer (FIRAS), and Diffuse Infrared Background Experiment (DIRBE)) are very important in measuring the big bang. DMR measures the isotropy of the cosmic background (direction of the radiation). FIRAS looks at the spectrum over the whole sky, searching for deviations, and DIRBE operates in the infrared part of the spectrum gathering evidence of the earliest galaxy formation. By special techniques, the radiation coming from the solar system will be distinguished from that of extragalactic origin. Unique graphics will be used to represent the temperature of the emitting material. A cosmic event will be modeled of such importance that it will affect cosmological theory for generations to come. EOS will monitor changes in the Earth's geophysics during a whole solar color cycle

Towards autonomous diagnostic systems with medical imaging

Democratizing access to high quality healthcare has highlighted the need for autonomous diagnostic systems that a non-expert can use. Remote communities, first responders and even deep space explorers will come to rely on medical imaging systems that will provide them with Point of Care diagnostic capabilities. This thesis introduces the building blocks that would enable the creation of such a system. Firstly, we present a case study in order to further motivate the need and requirements of autonomous diagnostic systems. This case study primarily concerns deep space exploration where astronauts cannot rely on communication with earth-bound doctors to help them through diagnosis, nor can they make the trip back to earth for treatment. Requirements and possible solutions about the major challenges faced with such an application are discussed. Moreover, this work describes how a system can explore its perceived environment by developing a Multi Agent Reinforcement Learning method that allows for implicit communication between the agents. Under this regime agents can share the knowledge that benefits them all in achieving their individual tasks. Furthermore, we explore how systems can understand the 3D properties of 2D depicted objects in a probabilistic way. In Part II, this work explores how to reason about the extracted information in a causally enabled manner. A critical view on the applications of causality in medical imaging, and its potential uses is provided. It is then narrowed down to estimating possible future outcomes and reasoning about counterfactual outcomes by embedding data on a pseudo-Riemannian manifold and constraining the latent space by using the relativistic concept of light cones. By formalizing an approach to estimating counterfactuals, a computationally lighter alternative to the abduction-action-prediction paradigm is presented through the introduction of Deep Twin Networks. Appropriate partial identifiability constraints for categorical variables are derived and the method is applied in a series of medical tasks involving structured data, images and videos. All methods are evaluated in a wide array of synthetic and real life tasks that showcase their abilities, often achieving state-of-the-art performance or matching the existing best performance while requiring a fraction of the computational cost.Open Acces