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Events for the 3rd week of August
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PhD Dissertation Defense - Weiwu Pang
Mon, Aug 12, 2024 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Toward Enabling Large-scale Outdoor Augmented Reality
Date: August 12, 2024
Location: SAL- Henry Salvatori Computer Science Center 213
Time: 10:00 AM - 12:00 PM
Committee members: Ramesh Govindan, Konstantinos Psounis, Mukund Raghothaman
Abstract:This thesis advances outdoor augmented reality (AR) by addressing critical challenges in urban situational awareness (Urban Situational Awareness) through the development of three innovative systems: Cooperative Infrastructure Perception (CIP), UbiPose, and SplatLoc. Urban Situational Awareness aims to enhance AR users’ understanding of their surroundings by accurately integrating dynamic digital content with the physical environment. This research focuses on two fundamental aspects of outdoor AR: dynamic content rendering and precise pose estimation. CIP leverages infrastructure LiDARs to provide real-time, multi-angular perception of urban spaces, enabling a "virtual see-through" capability. This system also introduces methods for extracting dynamic objects, such as pedestrians and vehicles, significantly improving AR accuracy and responsiveness. UbiPose uses aerial meshes to extend AR coverage, though it requires computationally intensive algorithms to address aerial image distortions. SplatLoc employs Gaussian Splatting (GSplat) from crowd-sourced street-level images, generating high-quality synthetic views for efficient and accurate pose estimation.
Two key contributions are highlighted. First, the exploration of how to extract dynamic content in urban settings, enhancing AR by detecting and representing moving objects. Second, the exploration of optimal map representations for outdoor AR pose estimation, balancing coverage, accuracy, and computational efficiency. The research proposes future directions, including creating high-quality GSplat using aerial images to improve availability and efficiency. It also discusses the need for efficient map update mechanisms to ensure timely and accurate real-world reflections. By addressing these challenges, this thesis lays the groundwork for more immersive and reliable outdoor AR applications, paving the way for transformative experiences in urban environments.Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Ellecia Williams
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Quantum Science & Technology Seminar - Xun Gao, Thursday, August 15th at 11am in EEB 248
Thu, Aug 15, 2024 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Xun Gao, University of Colorado Boulder
Talk Title: Interpretable Quantum Advantage in Neural Sequence Learning
Series: Quantum Science & Technology Seminar Series
Abstract: Quantum neural networks have been widely studied in recent years due to their potential practical utility and recent results showing their ability to efficiently express certain classical data. However, analytic results to date rely on assumptions and arguments from complexity theory. As a result, there is little intuition regarding the source of the expressive power of quantum neural networks or for which classes of classical data any advantage can be reasonably expected to hold. In this study, we examine the relative expressive power between a broad class of neural network sequence models and a class of recurrent models based on quantum mechanics. We demonstrate that quantum contextuality is the source of an unconditional memory separation in the expressivity of the two model classes. Using this intuition, we study the relative performance of our introduced model on a standard translation dataset exhibiting linguistic contextuality. Our quantum models outperform state-of-the-art classical models, even in practice. Finally, I will briefly discuss future directions of quantum neural networks and their potential connections to concepts in condensed matter physics, such as Berry phase and spin glass.
Biography: Xun Gao is an assistant professor at University of Colorado Boulder and an associate fellow at JILA. He got his PhD from Tsinghua University under the supervision of Luming Duan. Then he was a postdoc at Harvard University from Mikhail Lilian's group. His research interests are quantum computational advantage and quantum machine learning.
Host: Quntao Zhang, Wade Hsu, Mengjie Yu, Jonathan Habif & Eli Levenson-Falk
More Information: Xun Gao Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
