國立虎尾科技大學 |

Data-Driven Geometric Workflows for Camera Localization.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Data-Driven Geometric Workflows for Camera Localization./
作者:	Min, Zhixiang.
面頁冊數:	1 online resource (130 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9798380581202

Data-Driven Geometric Workflows for Camera Localization.
Min, Zhixiang.

Data-Driven Geometric Workflows for Camera Localization. - 1 online resource (130 pages)

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

Thesis (Ph.D.)--Stevens Institute of Technology, 2023.

Includes bibliographical references

Visual localization aims to estimate the spatial relationship between a camera and its environment based on captured visual media. However, the increasing demand for robustness in challenging environments and the requirement to solve semantic-centric problems present significant challenges for geometric localization workflows. Conventional methods rely on hand-crafted heuristics, which often struggle to meet the growing demands of such environments. On the other hand, emerging deep learning methods face issues with generalization and interpretability due to their inherently geometry-agnostic nature. To address these challenges, this dissertation presents a hybrid localization workflow that leverages both geometric and data-driven priors. We summarize four of our works that have achieved remarkable progress in terms of robustness, accuracy, and interpretability, by utilizing our proposed workflow. These four works span across different fields, including visual odometry, object localization, floor plan localization, and viewpoint learning.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380581202Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Floor plan localizationIndex Terms--Genre/Form:

554714
Electronic books.

Data-Driven Geometric Workflows for Camera Localization.
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520 $a Visual localization aims to estimate the spatial relationship between a camera and its environment based on captured visual media. However, the increasing demand for robustness in challenging environments and the requirement to solve semantic-centric problems present significant challenges for geometric localization workflows. Conventional methods rely on hand-crafted heuristics, which often struggle to meet the growing demands of such environments. On the other hand, emerging deep learning methods face issues with generalization and interpretability due to their inherently geometry-agnostic nature. To address these challenges, this dissertation presents a hybrid localization workflow that leverages both geometric and data-driven priors. We summarize four of our works that have achieved remarkable progress in terms of robustness, accuracy, and interpretability, by utilizing our proposed workflow. These four works span across different fields, including visual odometry, object localization, floor plan localization, and viewpoint learning.
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653 $a Visual odometry
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