Evaluation of Drop Shadows for Virtual Object Grasping in Augmented Reality

This paper presents the use of rendered visual cues as drop shadows and their impact on overall usability and accuracy of grasping interactions for monitor-based exocentric Augmented Reality (AR). We report on two conditions; grasping with drop shadows and without drop shadows and analyse a total of 1620 grasps of two virtual object types (cubes and spheres). We report on the accuracy of one grasp type, the Medium Wrap grasp, against Grasp Aperture (GAp), Grasp Displacement (GDisp), completion time and usability metrics from 30 participants. A comprehensive statistical analysis of the results is presented giving comparisons of the inclusion of drop shadows in AR grasping. Findings showed that the use of drop shadows increases usability of AR grasping while significantly decreasing task completion times. Furthermore, drop shadows also significantly improve user’s depth estimation of AR object position. However, this study also shows that using drop shadows does not improve user’s object size estimation, which remains as a problematic element in grasping AR interaction literature

Natural freehand grasping of virtual objects for augmented reality

Grasping is a primary form of interaction with the surrounding world, and is an intuitive interaction technique by nature due to the highly complex structure of the human hand. Translating this versatile interaction technique to Augmented Reality (AR) can provide interaction designers with more opportunities to implement more intuitive and realistic AR applications. The work presented in this thesis uses quantifiable measures to evaluate the accuracy and usability of natural grasping of virtual objects in AR environments, and presents methods for improving this natural form of interaction. Following a review of physical grasping parameters and current methods of mediating grasping interactions in AR, a comprehensive analysis of natural freehand grasping of virtual objects in AR is presented to assess the accuracy, usability and transferability of this natural form of grasping to AR environments. The analysis is presented in four independent user studies (120 participants, 30 participants for each study and 5760 grasping tasks in total), where natural freehand grasping performance is assessed for a range of virtual object sizes, positions and types in terms of accuracy of grasping, task completion time and overall system usability. Findings from the first user study in this work highlighted two key problems for natural grasping in AR; namely inaccurate depth estimation and inaccurate size estimation of virtual objects. Following the quantification of these errors, three different methods for mitigating user errors and assisting users during natural grasping were presented and analysed; namely dual view visual feedback, drop shadows and additional visual feedback when adding user based tolerances during interaction tasks. Dual view visual feedback was found to significantly improve user depth estimation, however this method also significantly increased task completion time. Drop shadows provided an alternative, and a more usable solution, to dual view visual feedback through significantly improving depth estimation, task completion time and the overall usability of natural grasping. User based tolerances negated the fundamental problem of inaccurate size estimation of virtual objects, through enabling users to perform natural grasping without the need of being highly accurate in their grasping performance, thus providing evidence that natural grasping can be usable in task based AR environments. Finally recommendations for allowing and further improving natural grasping interaction in AR environments are provided, along with guidelines for translating this form of natural grasping to other AR environments and user interfaces

Expanding tangible tabletop interfaces beyond the display

L’augment de popularitat de les taules i superfícies interactives està impulsant la recerca i la innovació en una gran varietat d’àrees, incloent-­‐hi maquinari, programari, disseny de la interacció i noves tècniques d’interacció. Totes, amb l’objectiu de promoure noves interfícies dotades d’un llenguatge més ric, potent i natural. Entre totes aquestes modalitats, la interacció combinada a sobre i per damunt de la superfície de la taula mitjançant tangibles i gestos és actualment una àrea molt prometedora. Aquest document tracta d’expandir les taules interactives més enllà de la superfície per mitjà de l’exploració i el desenvolupament d’un sistema o dispositiu enfocat des de tres vessants diferents: maquinari, programari i disseny de la interacció. Durant l’inici d’aquest document s’estudien i es resumeixen els diferents trets característics de les superfícies interactives tangibles convencionals o 2D i es presenten els treballs previs desenvolupats per l’autor en solucions de programari que acaben resultant en aplicacions que suggereixen l’ús de la tercera dimensió a les superfícies tangibles. Seguidament, es presenta un repàs del maquinari existent en aquest tipus d’interfícies per tal de concebre un dispositiu capaç de detectar gestos i generar visuals per sobre de la superfície, per introduir els canvis realitzats a un dispositiu existent, desenvolupat i cedit per Microsoft Reseach Cambridge. Per tal d’explotar tot el potencial d’aquest nou dispositiu, es desenvolupa un nou sistema de visió per ordinador que estén el seguiment d’objectes i mans en una superfície 2D a la detecció de mans, dits i etiquetes amb sis graus de llibertat per sobre la superfície incloent-­‐hi la interacció tangible i tàctil convencional a la superfície. Finalment, es presenta una eina de programari per a generar aplicacions per al nou sistema i es presenten un seguit d’aplicacions per tal de provar tot el desenvolupament generat al llarg de la tesi que es conclou presentant un seguit de gestos tant a la superfície com per sobre d’aquesta i situant-­‐los en una nova classificació que alhora recull la interacció convencional 2D i la interacció estesa per damunt de la superfície desenvolupada.The rising popularity of interactive tabletops and surfaces is spawning research and innovation in a wide variety of areas, including hardware and software technologies, interaction design and novel interaction techniques, all of which seek to promote richer, more powerful and more natural interaction modalities. Among these modalities, combined interaction on and above the surface, both with gestures and with tangible objects, is a very promising area. This dissertation is about expanding tangible and tabletops surfaces beyond the display by exploring and developing a system from the three different perspectives: hardware, software, and interaction design. This dissertation, studies and summarizes the distinctive affordances of conventional 2D tabletop devices, with a vast literature review and some additional use cases developed by the author for supporting these findings, and subsequently explores the novel and not yet unveiled potential affordances of 3D-­‐augmented tabletops. It overviews the existing hardware solutions for conceiving such a device, and applies the needed hardware modifications to an existing prototype developed and rendered to us by Microsoft Research Cambridge. For accomplishing the interaction purposes, it is developed a vision system for 3D interaction that extends conventional 2D tabletop tracking for the tracking of hand gestures, 6DoF markers and on-­‐surface finger interaction. It finishes by conceiving a complete software framework solution, for the development and implementation of such type of applications that can benefit from these novel 3D interaction techniques, and implements and test several software prototypes as proof of concepts, using this framework. With these findings, it concludes presenting continuous tangible interaction gestures and proposing a novel classification for 3D tangible and tabletop gestures

Visual Perception and Cognition in Image-Guided Intervention

Surgical image visualization and interaction systems can dramatically affect the efficacy and efficiency of surgical training, planning, and interventions. This is even more profound in the case of minimally-invasive surgery where restricted access to the operative field in conjunction with limited field of view necessitate a visualization medium to provide patient-specific information at any given moment. Unfortunately, little research has been devoted to studying human factors associated with medical image displays and the need for a robust, intuitive visualization and interaction interfaces has remained largely unfulfilled to this day. Failure to engineer efficient medical solutions and design intuitive visualization interfaces is argued to be one of the major barriers to the meaningful transfer of innovative technology to the operating room. This thesis was, therefore, motivated by the need to study various cognitive and perceptual aspects of human factors in surgical image visualization systems, to increase the efficiency and effectiveness of medical interfaces, and ultimately to improve patient outcomes. To this end, we chose four different minimally-invasive interventions in the realm of surgical training, planning, training for planning, and navigation: The first chapter involves the use of stereoendoscopes to reduce morbidity in endoscopic third ventriculostomy. The results of this study suggest that, compared with conventional endoscopes, the detection of the basilar artery on the surface of the third ventricle can be facilitated with the use of stereoendoscopes, increasing the safety of targeting in third ventriculostomy procedures. In the second chapter, a contour enhancement technique is described to improve preoperative planning of arteriovenous malformation interventions. The proposed method, particularly when combined with stereopsis, is shown to increase the speed and accuracy of understanding the spatial relationship between vascular structures. In the third chapter, an augmented-reality system is proposed to facilitate the training of planning brain tumour resection. The results of our user study indicate that the proposed system improves subjects\u27 performance, particularly novices\u27, in formulating the optimal point of entry and surgical path independent of the sensorimotor tasks performed. In the last chapter, the role of fully-immersive simulation environments on the surgeons\u27 non-technical skills to perform vertebroplasty procedure is investigated. Our results suggest that while training surgeons may increase their technical skills, the introduction of crisis scenarios significantly disturbs the performance, emphasizing the need of realistic simulation environments as part of training curriculum

Bringing the Physical to the Digital

This dissertation describes an exploration of digital tabletop interaction styles, with the ultimate goal of informing the design of a new model for tabletop interaction. In the context of this thesis the term digital tabletop refers to an emerging class of devices that afford many novel ways of interaction with the digital. Allowing users to directly touch information presented on large, horizontal displays. Being a relatively young field, many developments are in flux; hardware and software change at a fast pace and many interesting alternative approaches are available at the same time. In our research we are especially interested in systems that are capable of sensing multiple contacts (e.g., fingers) and richer information such as the outline of whole hands or other physical objects. New sensor hardware enable new ways to interact with the digital. When embarking into the research for this thesis, the question which interaction styles could be appropriate for this new class of devices was a open question, with many equally promising answers. Many everyday activities rely on our hands ability to skillfully control and manipulate physical objects. We seek to open up different possibilities to exploit our manual dexterity and provide users with richer interaction possibilities. This could be achieved through the use of physical objects as input mediators or through virtual interfaces that behave in a more realistic fashion. In order to gain a better understanding of the underlying design space we choose an approach organized into two phases. First, two different prototypes, each representing a specific interaction style – namely gesture-based interaction and tangible interaction – have been implemented. The flexibility of use afforded by the interface and the level of physicality afforded by the interface elements are introduced as criteria for evaluation. Each approaches’ suitability to support the highly dynamic and often unstructured interactions typical for digital tabletops is analyzed based on these criteria. In a second stage the learnings from these initial explorations are applied to inform the design of a novel model for digital tabletop interaction. This model is based on the combination of rich multi-touch sensing and a three dimensional environment enriched by a gaming physics simulation. The proposed approach enables users to interact with the virtual through richer quantities such as collision and friction. Enabling a variety of fine-grained interactions using multiple fingers, whole hands and physical objects. Our model makes digital tabletop interaction even more “natural”. However, because the interaction – the sensed input and the displayed output – is still bound to the surface, there is a fundamental limitation in manipulating objects using the third dimension. To address this issue, we present a technique that allows users to – conceptually – pick objects off the surface and control their position in 3D. Our goal has been to define a technique that completes our model for on-surface interaction and allows for “as-direct-as possible” interactions. We also present two hardware prototypes capable of sensing the users’ interactions beyond the table’s surface. Finally, we present visual feedback mechanisms to give the users the sense that they are actually lifting the objects off the surface. This thesis contributes on various levels. We present several novel prototypes that we built and evaluated. We use these prototypes to systematically explore the design space of digital tabletop interaction. The flexibility of use afforded by the interaction style is introduced as criterion alongside the user interface elements’ physicality. Each approaches’ suitability to support the highly dynamic and often unstructured interactions typical for digital tabletops are analyzed. We present a new model for tabletop interaction that increases the fidelity of interaction possible in such settings. Finally, we extend this model so to enable as direct as possible interactions with 3D data, interacting from above the table’s surface

Distance mis-estimations can be reduced with specific shadow locations

Shadows in physical space are copious, yet the impact of specific shadow placement and their abundance is yet to be determined in virtual environments. This experiment aimed to identify whether a target’s shadow was used as a distance indicator in the presence of binocular distance cues. Six lighting conditions were created and presented in virtual reality for participants to perform a perceptual matching task. The task was repeated in a cluttered and sparse environment, where the number of cast shadows (and their placement) varied. Performance in this task was measured by the directional bias of distance estimates and variability of responses. No significant difference was found between the sparse and cluttered environments, however due to the large amount of variance, one explanation is that some participants utilised the clutter objects as anchors to aid them, while others found them distracting. Under-setting of distances was found in all conditions and environments, as predicted. Having an ambient light source produced the most variable and inaccurate estimates of distance, whereas lighting positioned above the target reduced the mis-estimation of distances perceived

Deep learning for object detection in robotic grasping contexts

Dans la dernière décennie, les approches basées sur les réseaux de neurones convolutionnels sont devenus les standards pour la plupart des tâches en vision numérique. Alors qu'une grande partie des méthodes classiques de vision étaient basées sur des règles et algorithmes, les réseaux de neurones sont optimisés directement à partir de données d'entraînement qui sont étiquetées pour la tâche voulue. En pratique, il peut être difficile d'obtenir une quantité su sante de données d'entraînement ou d'interpréter les prédictions faites par les réseaux. Également, le processus d'entraînement doit être recommencé pour chaque nouvelle tâche ou ensemble d'objets. Au final, bien que très performantes, les solutions basées sur des réseaux de neurones peuvent être difficiles à mettre en place. Dans cette thèse, nous proposons des stratégies visant à contourner ou solutionner en partie ces limitations en contexte de détection d'instances d'objets. Premièrement, nous proposons d'utiliser une approche en cascade consistant à utiliser un réseau de neurone comme pré-filtrage d'une méthode standard de "template matching". Cette façon de faire nous permet d'améliorer les performances de la méthode de "template matching" tout en gardant son interprétabilité. Deuxièmement, nous proposons une autre approche en cascade. Dans ce cas, nous proposons d'utiliser un réseau faiblement supervisé pour générer des images de probabilité afin d'inférer la position de chaque objet. Cela permet de simplifier le processus d'entraînement et diminuer le nombre d'images d'entraînement nécessaires pour obtenir de bonnes performances. Finalement, nous proposons une architecture de réseau de neurones ainsi qu'une procédure d'entraînement permettant de généraliser un détecteur d'objets à des objets qui ne sont pas vus par le réseau lors de l'entraînement. Notre approche supprime donc la nécessité de réentraîner le réseau de neurones pour chaque nouvel objet.In the last decade, deep convolutional neural networks became a standard for computer vision applications. As opposed to classical methods which are based on rules and hand-designed features, neural networks are optimized and learned directly from a set of labeled training data specific for a given task. In practice, both obtaining sufficient labeled training data and interpreting network outputs can be problematic. Additionnally, a neural network has to be retrained for new tasks or new sets of objects. Overall, while they perform really well, deployment of deep neural network approaches can be challenging. In this thesis, we propose strategies aiming at solving or getting around these limitations for object detection. First, we propose a cascade approach in which a neural network is used as a prefilter to a template matching approach, allowing an increased performance while keeping the interpretability of the matching method. Secondly, we propose another cascade approach in which a weakly-supervised network generates object-specific heatmaps that can be used to infer their position in an image. This approach simplifies the training process and decreases the number of required training images to get state-of-the-art performances. Finally, we propose a neural network architecture and a training procedure allowing detection of objects that were not seen during training, thus removing the need to retrain networks for new objects

Interactive tabletops in education

Interactive tabletops are gaining increased attention from CSCL researchers. This paper analyses the relation between this technology and teaching and learning processes. At a global level, one could argue that tabletops convey a socio-constructivist flavor: they support small teams that solve problems by exploring multiple solutions. The development of tabletop applications also witnesses the growing importance of face-to-face collaboration in CSCL and acknowledges the physicality of learning. However, this global analysis is insufficient. To analyze the educational potential of tabletops in education, we present 33 points that should be taken into consideration. These points are structured on four levels: individual user-system interaction, teamwork, classroom orchestration, and socio-cultural contexts. God lies in the detail