Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
Browse
3 results
Search Results
Item Design Considerations for Remote Expert Guidance Using Extended Reality in Skilled Hobby Settings.(2023) Maddali, Hanuma Teja; Lazar, Amanda; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)As compact and lightweight extended reality (XR) devices become increasingly available, research is being reinvigorated in a number of areas. One such area for XR applications involves remote collaboration, where a remote expert can assist, train, or share skills or ideas with a local user to solve a real-world task. For example, researchers have looked into real-time expert assistance and professional training of novices in skilled physical activities such as field servicing and surgical training. Even as our understanding of XR for remote collaboration in professional settings advances, an area that has not been examined is how XR can support such expert-novice collaboration in skilled hobby activities (e.g., gardening, woodworking, and knitting). Metrics such as task accuracy or efficiency are often less important than in professional settings. Instead, other dimensions, such as social connectedness and emotional experience, may become central dimensions that inform system design. In my dissertation, I examine how the XR environment can be designed to support the sharing of skills in hobby activities. I have selected gardening as a hobby activity to examine remote skill-sharing in XR between experts and novices. Like in other hobby activities, learning gardening practices remotely can involve asynchronous, text, or image/video-based communication on Facebook groups. While these may be helpful for individual questions, they do not capture the social, affective, and embodied dimensions of gaining expertise as a novice through situated learning in the garden. These dimensions can also be central to the experience of the activity. In my work, I seek to understand how to design a social XR environment that captures these dimensions in ways that are acceptable and useful to intergenerational expert-novice gardener groups. Through my dissertation work, I answer the following research questions:1. How do practitioners of a particular hobby exhibit sociality and what kinds of social interactions facilitate skill-sharing? What are some key opportunities for computer-supported collaborative work in this space? 2. What are practitioners' perceptions of using XR for skill-sharing? What are the important dimensions of the design space and design scenarios for social XR systems? 3. How do practitioners use different components of the activity space (e.g., tools or sensory stimuli) and their affordances to facilitate social connection? What context is essential to capture when reconstructing these objects virtually for remote interaction in XR (e.g., interactivity and realism)? 4. What are some design considerations for XR to support accessible interactions that reflect the values and goals of an intergenerational group?Item Dense 3D Reconstructions from Sparse Visual Data(2022) Hu, Tao; Zwicker, Matthias; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)3D reconstruction, the problem of estimating the complete geometry or appearance of objects from partial observations (e.g., several RGB images, partial shapes, videos), serves as a building block in many vision, graphics, and robotics applications such as 3D scanning, autonomous driving, 3D modeling, augmented reality (AR) and virtual reality (VR). However, it is very challenging for machines to recover 3D geometry from such sparse data due to occlusions, and irregularity and complexity of 3D objects. To solve these, in this dissertation, we explore learning-based 3D reconstruction methods for different 3D object representations on different tasks: 3D reconstructions of static objects and dynamic human body from limited data. For the 3D reconstructions of static objects, we propose a multi-view representation of 3D shapes, which utilizes a set of multi-view RGB images or depth maps to represent a 3D shape. We first explore the multi-view representation for shape completion tasks and develop deep learning methods to generate dense and high-resolution point clouds from partial observations. Yet one problem with the multi-view representation is the inconsistency among different views. To solve this problem, we propose a multi-view consistency optimization strategy to encourage consistency for shape completion in inference stage. Third, the extension of multi-view representation for dense 3D geometry and texture reconstructions from single RGB images will be presented. Capturing and rendering realistic human appearances under varying poses and viewpoints is an important goal in computer vision and graphics. In the second part, we will introduce some techniques to create 3D virtual human avatars with limited data (e.g., videos). We propose implicit representations of motion, texture, and geometry for human modeling, and utilize neural rendering techniques for free view synthesis of dynamic articulated human body. Our learned human avatars are photorealistic and fully controllable (pose, shape, viewpoints, etc.), which can be used in free-viewpoint video generation, animation, shape editing, telepresence, and AR/VR. Our proposed methods can learn end-to-end 3D reconstructions from 2D image or video signals. We hope these learning-based methods will assist in perceiving and reconstructing the 3D world for future AI systems.Item Single-View 3D Reconstruction of Animals(2017) Kim, Angjoo; Jacobs, David W; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Humans have a remarkable ability to infer the 3D shape of objects from just a single image. Even for complex and non-rigid objects like people and animals, from just a single picture we can say much about its 3D shape, configuration and even the viewpoint that the photo was taken from. Today, the same cannot be said for computers – the existing solutions are limited, particularly for highly articulated and deformable objects. Hence, the purpose of this thesis is to develop methods for single-view 3D reconstruction of non-rigid objects, specifically for people and animals. Our goal is to recover a full 3D surface model of these objects from a single unconstrained image. The ability to do so, even with some user interaction, will have a profound impact in AR/VR and the entertainment industry. Immediate applications are virtual avatars and pets, virtual clothes fitting, immersive games, as well as applications in biology, neuroscience, ecology, and farming. However, this is a challenging problem because these objects can appear in many different forms. This thesis begins by providing the first fully automatic solution for recovering a 3D mesh of a human body from a single image. Our solution follows the classical paradigm of bottom-up estimation followed by top-down verification. The key is to solve for the mostly likely 3D model that explains the image observations by using powerful priors. The rest of the thesis explores how to extend a similar approach for other animals. Doing so reveals novel challenges whose common thread is the lack of specialized data. For solving the bottom-up estimation problem well, current methods rely on the availability of human supervision in the form of 2D part annotations. However, these annotations do not exist in the same scale for animals. We deal with this problem by means of data synthesis for the case of fine-grained categories such as bird species. There is also little work that systematically addresses the 3D scanning of animals, which almost all prior works require for learning a deformable 3D model. We propose a solution to learn a 3D deformable model from a set of annotated 2D images with a template 3D mesh and from a few set of 3D toy figurine scans. We show results on birds, house cats, horses, cows, dogs, big cats, and even hippos. This thesis makes steps towards a fully automatic system for single-view 3D reconstruction of animals. We hope this work inspires more future research in this direction.