Merging the Real and the Virtual: An Exploration of Interaction Methods to Blend Realities

We investigate, build, and design interaction methods to merge the real with the virtual. An initial investigation looks at spatial augmented reality (SAR) and its effects on pointing with a real mobile phone. A study reveals a set of trade-offs between the raycast, viewport, and direct pointing techniques. To further investigate the manipulation of virtual content within a SAR environment, we design an interaction technique that utilizes the distance that a user holds mobile phone away from their body. Our technique enables pushing virtual content from a mobile phone to an external SAR environment, interact with that content, rotate-scale-translate it, and pull the content back into the mobile phone. This is all done in a way that ensures seamless transitions between the real environment of the mobile phone and the virtual SAR environment. To investigate the issues that occur when the physical environment is hidden by a fully immersive virtual reality (VR) HMD, we design and investigate a system that merges a realtime 3D reconstruction of the real world with a virtual environment. This allows users to freely move, manipulate, observe, and communicate with people and objects situated in their physical reality without losing their sense of immersion or presence inside a virtual world. A study with VR users demonstrates the affordances provided by the system and how it can be used to enhance current VR experiences. We then move to AR, to investigate the limitations of optical see-through HMDs and the problem of communicating the internal state of the virtual world with unaugmented users. To address these issues and enable new ways to visualize, manipulate, and share virtual content, we propose a system that combines a wearable SAR projector. Demonstrations showcase ways to utilize the projected and head-mounted displays together, such as expanding field of view, distributing content across depth surfaces, and enabling bystander collaboration. We then turn to videogames to investigate how spectatorship of these virtual environments can be enhanced through expanded video rendering techniques. We extract and combine additional data to form a cumulative 3D representation of the live game environment for spectators, which enables each spectator to individually control a personal view into the stream while in VR. A study shows that users prefer spectating in VR when compared with a comparable desktop rendering

Robotic Cameraman for Augmented Reality based Broadcast and Demonstration

In recent years, a number of large enterprises have gradually begun to use vari-ous Augmented Reality technologies to prominently improve the audiences’ view oftheir products. Among them, the creation of an immersive virtual interactive scenethrough the projection has received extensive attention, and this technique refers toprojection SAR, which is short for projection spatial augmented reality. However,as the existing projection-SAR systems have immobility and limited working range,they have a huge difficulty to be accepted and used in human daily life. Therefore,this thesis research has proposed a technically feasible optimization scheme so thatit can be practically applied to AR broadcasting and demonstrations. Based on three main techniques required by state-of-art projection SAR applica-tions, this thesis has created a novel mobile projection SAR cameraman for ARbroadcasting and demonstration. Firstly, by combining the CNN scene parsingmodel and multiple contour extractors, the proposed contour extraction pipelinecan always detect the optimal contour information in non-HD or blurred images.This algorithm reduces the dependency on high quality visual sensors and solves theproblems of low contour extraction accuracy in motion blurred images. Secondly, aplane-based visual mapping algorithm is introduced to solve the difficulties of visualmapping in these low-texture scenarios. Finally, a complete process of designing theprojection SAR cameraman robot is introduced. This part has solved three mainproblems in mobile projection-SAR applications: (i) a new method for marking con-tour on projection model is proposed to replace the model rendering process. Bycombining contour features and geometric features, users can identify objects oncolourless model easily. (ii) a camera initial pose estimation method is developedbased on visual tracking algorithms, which can register the start pose of robot to thewhole scene in Unity3D. (iii) a novel data transmission approach is introduced to establishes a link between external robot and the robot in Unity3D simulation work-space. This makes the robotic cameraman can simulate its trajectory in Unity3D simulation work-space and project correct virtual content. Our proposed mobile projection SAR system has made outstanding contributionsto the academic value and practicality of the existing projection SAR technique. Itfirstly solves the problem of limited working range. When the system is running ina large indoor scene, it can follow the user and project dynamic interactive virtualcontent automatically instead of increasing the number of visual sensors. Then,it creates a more immersive experience for audience since it supports the user hasmore body gestures and richer virtual-real interactive plays. Lastly, a mobile systemdoes not require up-front frameworks and cheaper and has provided the public aninnovative choice for indoor broadcasting and exhibitions

Advances in Simultaneous Localization and Mapping in Confined Underwater Environments Using Sonar and Optical Imaging.

This thesis reports on the incorporation of surface information into a probabilistic simultaneous localization and mapping (SLAM) framework used on an autonomous underwater vehicle (AUV) designed for underwater inspection. AUVs operating in cluttered underwater environments, such as ship hulls or dams, are commonly equipped with Doppler-based sensors, which---in addition to navigation---provide a sparse representation of the environment in the form of a three-dimensional (3D) point cloud. The goal of this thesis is to develop perceptual algorithms that take full advantage of these sparse observations for correcting navigational drift and building a model of the environment. In particular, we focus on three objectives. First, we introduce a novel representation of this 3D point cloud as collections of planar features arranged in a factor graph. This factor graph representation probabalistically infers the spatial arrangement of each planar segment and can effectively model smooth surfaces (such as a ship hull). Second, we show how this technique can produce 3D models that serve as input to our pipeline that produces the first-ever 3D photomosaics using a two-dimensional (2D) imaging sonar. Finally, we propose a model-assisted bundle adjustment (BA) framework that allows for robust registration between surfaces observed from a Doppler sensor and visual features detected from optical images. Throughout this thesis, we show methods that produce 3D photomosaics using a combination of triangular meshes (derived from our SLAM framework or given a-priori), optical images, and sonar images. Overall, the contributions of this thesis greatly increase the accuracy, reliability, and utility of in-water ship hull inspection with AUVs despite the challenges they face in underwater environments. We provide results using the Hovering Autonomous Underwater Vehicle (HAUV) for autonomous ship hull inspection, which serves as the primary testbed for the algorithms presented in this thesis. The sensor payload of the HAUV consists primarily of: a Doppler velocity log (DVL) for underwater navigation and ranging, monocular and stereo cameras, and---for some applications---an imaging sonar.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120750/1/paulozog_1.pd

Freeform 3D interactions in everyday environments

PhD ThesisPersonal computing is continuously moving away from traditional input using mouse and keyboard, as new input technologies emerge. Recently, natural user interfaces (NUI) have led to interactive systems that are inspired by our physical interactions in the real-world, and focus on enabling dexterous freehand input in 2D or 3D. Another recent trend is Augmented Reality (AR), which follows a similar goal to further reduce the gap between the real and the virtual, but predominately focuses on output, by overlaying virtual information onto a tracked real-world 3D scene. Whilst AR and NUI technologies have been developed for both immersive 3D output as well as seamless 3D input, these have mostly been looked at separately. NUI focuses on sensing the user and enabling new forms of input; AR traditionally focuses on capturing the environment around us and enabling new forms of output that are registered to the real world. The output of NUI systems is mainly presented on a 2D display, while the input technologies for AR experiences, such as data gloves and body-worn motion trackers are often uncomfortable and restricting when interacting in the real world. NUI and AR can be seen as very complimentary, and bringing these two fields together can lead to new user experiences that radically change the way we interact with our everyday environments. The aim of this thesis is to enable real-time, low latency, dexterous input and immersive output without heavily instrumenting the user. The main challenge is to retain and to meaningfully combine the positive qualities that are attributed to both NUI and AR systems. I review work in the intersecting research fields of AR and NUI, and explore freehand 3D interactions with varying degrees of expressiveness, directness and mobility in various physical settings. There a number of technical challenges that arise when designing a mixed NUI/AR system, which I will address is this work: What can we capture, and how? How do we represent the real in the virtual? And how do we physically couple input and output? This is achieved by designing new systems, algorithms, and user experiences that explore the combination of AR and NUI

ElasticFusion: real-time dense SLAM and light source estimation

We present a novel approach to real-time dense visual SLAM. Our system is capable of capturing comprehensive dense globally consistent surfel-based maps of room scale environments and beyond explored using an RGB-D camera in an incremental online fashion, without pose graph optimisation or any post-processing steps. This is accomplished by using dense frame-tomodel camera tracking and windowed surfel-based fusion coupled with frequent model refinement through non-rigid surface deformations. Our approach applies local model-to-model surface loop closure optimisations as often as possible to stay close to the mode of the map distribution, while utilising global loop closure to recover from arbitrary drift and maintain global consistency. In the spirit of improving map quality as well as tracking accuracy and robustness, we furthermore explore a novel approach to real-time discrete light source detection. This technique is capable of detecting numerous light sources in indoor environments in real-time as a user handheld camera explores the scene. Absolutely no prior information about the scene or number of light sources is required. By making a small set of simple assumptions about the appearance properties of the scene our method can incrementally estimate both the quantity and location of multiple light sources in the environment in an online fashion. Our results demonstrate that our technique functions well in many different environments and lighting configurations. We show that this enables (a) more realistic augmented reality (AR) rendering; (b) a richer understanding of the scene beyond pure geometry and; (c) more accurate and robust photometric trackin

Machine Learning Approaches to Human Body Shape Analysis

Soft biometrics, biomedical sciences, and many other fields of study pay particular attention to the study of the geometric description of the human body, and its variations. Although multiple contributions, the interest is particularly high given the non-rigid nature of the human body, capable of assuming different poses, and numerous shapes due to variable body composition. Unfortunately, a well-known costly requirement in data-driven machine learning, and particularly in the human-based analysis, is the availability of data, in the form of geometric information (body measurements) with related vision information (natural images, 3D mesh, etc.). We introduce a computer graphics framework able to generate thousands of synthetic human body meshes, representing a population of individuals with stratified information: gender, Body Fat Percentage (BFP), anthropometric measurements, and pose. This contribution permits an extensive analysis of different bodies in different poses, avoiding the demanding, and expensive acquisition process. We design a virtual environment able to take advantage of the generated bodies, to infer the body surface area (BSA) from a single view. The framework permits to simulate the acquisition process of newly introduced RGB-D devices disentangling different noise components (sensor noise, optical distortion, body part occlusions). Common geometric descriptors in soft biometric, as well as in biomedical sciences, are based on body measurements. Unfortunately, as we prove, these descriptors are not pose invariant, constraining the usability in controlled scenarios. We introduce a differential geometry approach assuming body pose variations as isometric transformations of the body surface, and body composition changes covariant to the body surface area. This setting permits the use of the Laplace-Beltrami operator on the 2D body manifold, describing the body with a compact, efficient, and pose invariant representation. We design a neural network architecture able to infer important body semantics from spectral descriptors, closing the gap between abstract spectral features, and traditional measurement-based indices. Studying the manifold of body shapes, we propose an innovative generative adversarial model able to learn the body shapes. The method permits to generate new bodies with unseen geometries as a walk on the latent space, constituting a significant advantage over traditional generative methods

Computational Multimedia for Video Self Modeling

Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of oneself. This is the idea behind the psychological theory of self-efficacy - you can learn or model to perform certain tasks because you see yourself doing it, which provides the most ideal form of behavior modeling. The effectiveness of VSM has been demonstrated for many different types of disabilities and behavioral problems ranging from stuttering, inappropriate social behaviors, autism, selective mutism to sports training. However, there is an inherent difficulty associated with the production of VSM material. Prolonged and persistent video recording is required to capture the rare, if not existed at all, snippets that can be used to string together in forming novel video sequences of the target skill. To solve this problem, in this dissertation, we use computational multimedia techniques to facilitate the creation of synthetic visual content for self-modeling that can be used by a learner and his/her therapist with a minimum amount of training data. There are three major technical contributions in my research. First, I developed an Adaptive Video Re-sampling algorithm to synthesize realistic lip-synchronized video with minimal motion jitter. Second, to denoise and complete the depth map captured by structure-light sensing systems, I introduced a layer based probabilistic model to account for various types of uncertainties in the depth measurement. Third, I developed a simple and robust bundle-adjustment based framework for calibrating a network of multiple wide baseline RGB and depth cameras

A rigid 3D registration framework of women body RGB-D images

O trabalho realizado foca-se no melhoramento e automatização da framework desenvolvida para o projeto PICTURE do grupo de investigação VCMI do INESC-TEC. O principal objetivo tem que ver com a criação de modelos 3D do torso de pacientes com cancro da mama, a partir de dados adquiridos com sensores RGB-D low-cost, como o Kinect da Microsoft. As contribuições da tese passam pela criação de algoritmos para a automatização de processos, tais como: seleção da pose da mulher, segmentação do torso, remoção de ruído e o registo de múltiplas nuvens de pontos. O trabalho tem decorrido de forma aproximada o plano traçado no relatório de PDI, e neste momento encontra-se numa fase de finalização da implementação e testes para validação dos algoritmos desenvolvidos. Por outro lado, já foi iniciado o processo de escrita do documento final da Dissertação

Sparse octree algorithms for scalable dense volumetric tracking and mapping

This thesis is concerned with the problem of Simultaneous Localisation and Mapping (SLAM), the task of localising an agent within an unknown environment and at the same time building a representation of it. In particular, we tackle the fundamental scalability limitations of dense volumetric SLAM systems. We do so by proposing a highly efficient hierarchical data-structure based on octrees together with a set of algorithms to support the most compute-intensive operations in typical volumetric reconstruction pipelines. We employ our hierarchical representation in a novel dense pipeline based on occupancy probabilities. Crucially, the complete space representation encoded by the octree enables to demonstrate a fully integrated system in which tracking, mapping and occupancy queries can be performed seamlessly on a single coherent representation. While achieving accuracy either at par or better than the current state-of-the-art, we demonstrate run-time performance of at least an order of magnitude better than currently available hierarchical data-structures. Finally, we introduce a novel multi-scale reconstruction system that exploits our octree hierarchy. By adaptively selecting the appropriate scale to match the effective sensor resolution in both integration and rendering, we demonstrate better reconstruction results and tracking accuracy compared to single-resolution grids. Furthermore, we achieve much higher computational performance by propagating information up and down the tree in a lazy fashion, which allow us to reduce the computational load when updating distant surfaces. We have released our software as an open-source library, named supereight, which is freely available for the benefit of the wider community. One of the main advantages of our library is its flexibility. By carefully providing a set of algorithmic abstractions, supereight enables SLAM practitioners to freely experiment with different map representations with no intervention on the back-end library code and crucially, preserving performance. Our work has been adopted by robotics researchers in both academia and industry.Open Acces

GIVE-ME: Gamification In Virtual Environments for Multimodal Evaluation - A Framework

In the last few decades, a variety of assistive technologies (AT) have been developed to improve the quality of life of visually impaired people. These include providing an independent means of travel and thus better access to education and places of work. There is, however, no metric for comparing and benchmarking these technologies, especially multimodal systems. In this dissertation, we propose GIVE-ME: Gamification In Virtual Environments for Multimodal Evaluation, a framework which allows for developers and consumers to assess their technologies in a functional and objective manner. This framework is based on three foundations: multimodality, gamification, and virtual reality. It facilitates fuller and more controlled data collection, rapid prototyping and testing of multimodal ATs, benchmarking heterogeneous ATs, and conversion of these evaluation tools into simulation or training tools. Our contributions include: (1) a unified evaluation framework: via developing an evaluative approach for multimodal visual ATs; (2) a sustainable evaluation: by employing virtual environments and gamification techniques to create engaging games for users, while collecting experimental data for analysis; (3) a novel psychophysics evaluation: enabling researchers to conduct psychophysics evaluation despite the experiment being a navigational task; and (4) a novel collaborative environment: enabling developers to rapid prototype and test their ATs with users in an early stakeholder involvement that fosters communication between developers and users. This dissertation first provides a background in assistive technologies and motivation for the framework. This is followed by detailed description of the GIVE-ME Framework, with particular attention to its user interfaces, foundations, and components. Then four applications are presented that describe how the framework is applied. Results and discussions are also presented for each application. Finally, both conclusions and a few directions for future work are presented in the last chapter