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You searched for +publisher:"University of Texas – Austin" +contributor:("Snavely, Noah"). One record found.

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University of Texas – Austin

1. -2711-6738. Learning for 360° video compression, recognition, and display.

Degree: PhD, Computer Science, 2019, University of Texas – Austin

360° cameras are a core building block of the Virtual Reality (VR) and Augmented Reality (AR) technology that bridges the real and digital worlds. It allows us to build virtual environments for VR/AR applications from the real world easily by capturing the entire visual world surrounding the camera simultaneously. With the rapid growth of VR/AR technology, the availability and popularity of 360° cameras are also growing faster than ever. People now create, share, and watch 360° content in their everyday life, and the amount of 360° content is increasing rapidly. While 360° cameras offer tremendous new possibilities in various domains, they also introduce new technical challenges. These challenges span over the entire 360° video production pipeline, ranging all the way from video capturing to high level applications. For example, the 360° field-of-view makes it difficult to display the content to users, and the distortion in the planar projection degrades the performance of both the compression and visual recognition algorithms. Many of the challenges remain unsolved or even unexplored, which prohibits people from exploiting the full potential of the new media. This leads to a dire need for a more mature 360° video production pipeline like those for traditional media. To this end, my thesis targets three fundamental challenges in 360° production—video compression, visual recognition, and 360° video display. Because a proper compressed format is the foundation of all video technologies and applications, I begin with improving 360° video compression. It has been shown that existing video codecs do not perform well on 360° video, and 360° video compression standard is under rapid development. Complementary to the ongoing progress in 360° video compression standards, I propose to exploit the orientation of the 360° video projection for a better compression rate. The method explores a new dimension in video compression and is compatible with existing compression technologies. It reduces video sizes to allow easy storage and transmission of 360° videos. Besides being able to collect and distribute 360° content, another prerequisite for building advanced applications on top of the new media is the ability to analyze the visual content. Therefore, I next study visual recognition on 360° imagery. I propose a general approach that transfers an existing Convolutional Neural Network (CNN) trained on perspective images to 360° imagery. It allows us to transfer knowledge from perspective images to 360° images, including both the network architecture and training data. Compared with existing strategies for applying existing CNN models on 360° data, the method sacrifices neither accuracy nor efficiency and does not need any additional annotation effort. The method allows us to perform visual recognition on the new format given an existing CNN model with zero manual labor. After building the basis for 360° video applications, I finally tackle one of the most important applications of 360° video: displaying the video content to users.… Advisors/Committee Members: Grauman, Kristen Lorraine, 1979- (advisor), Snavely, Noah (committee member), Huang, Qixing (committee member), Niekum, Scott (committee member).

Subjects/Keywords: 360° video; Omnidirectional media; Video analysis

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APA (6th Edition):

-2711-6738. (2019). Learning for 360° video compression, recognition, and display. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/5848

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Chicago Manual of Style (16th Edition):

-2711-6738. “Learning for 360° video compression, recognition, and display.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed September 18, 2020. http://dx.doi.org/10.26153/tsw/5848.

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MLA Handbook (7th Edition):

-2711-6738. “Learning for 360° video compression, recognition, and display.” 2019. Web. 18 Sep 2020.

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Author name may be incomplete

Vancouver:

-2711-6738. Learning for 360° video compression, recognition, and display. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Sep 18]. Available from: http://dx.doi.org/10.26153/tsw/5848.

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Author name may be incomplete

Council of Science Editors:

-2711-6738. Learning for 360° video compression, recognition, and display. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/5848

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

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