國立虎尾科技大學 |

3D Reconstruction from Passive Sensors.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	3D Reconstruction from Passive Sensors./
作者:	He, Fangning.
面頁冊數:	1 online resource (213 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
Contained By:	Dissertation Abstracts International79-03B(E).
標題:	Civil engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355255928

3D Reconstruction from Passive Sensors.
He, Fangning.

3D Reconstruction from Passive Sensors. - 1 online resource (213 pages)

Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

In the past few decades, image-based 3D reconstruction has been widely adopted in a variety of applications for deriving accurate 3D models of the detected objects. Although current commercial software has automated the process of extract 3D information from 2D images, a transparent system, which can be incorporated with different user-defined constraints, is still the optimum option for many applications. In this regard, this dissertation introduces a generic framework for image-based 3D reconstruction from RGB-based frame cameras with different configurations (i.e., single or multi-camera systems). In this generic framework, a fully automated aerial triangulation procedure, which assumes the availability of prior information regarding the platform trajectory, is initially proposed for accurate 3D reconstruction of images that are captured by a single camera. Then, an automated 3D reconstruction procedure is introduced for image-based point cloud generation from multi-camera systems. Similar to the automated aerial triangulation, this procedure also takes advantage of the prior trajectory information to facilitate the image-based 3D reconstruction process. Since both proposed procedures are based on a Structure-from-Motion (SfM) framework, they are capable of dealing with image-based 3D reconstruction in the absence of GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) information or only in the presence of less-accurate POS (Position Orientation System) information. Finally, an object space-based approach is proposed to achieve more efficient image-based dense point cloud generation. Experimental results from real datasets demonstrate the feasibility of the proposed procedures in providing accurate image-based 3D reconstruction from images acquired with different configurations.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355255928Subjects--Topical Terms:

561339
Civil engineering.
Index Terms--Genre/Form:

554714
Electronic books.

3D Reconstruction from Passive Sensors.
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520 $a In the past few decades, image-based 3D reconstruction has been widely adopted in a variety of applications for deriving accurate 3D models of the detected objects. Although current commercial software has automated the process of extract 3D information from 2D images, a transparent system, which can be incorporated with different user-defined constraints, is still the optimum option for many applications. In this regard, this dissertation introduces a generic framework for image-based 3D reconstruction from RGB-based frame cameras with different configurations (i.e., single or multi-camera systems). In this generic framework, a fully automated aerial triangulation procedure, which assumes the availability of prior information regarding the platform trajectory, is initially proposed for accurate 3D reconstruction of images that are captured by a single camera. Then, an automated 3D reconstruction procedure is introduced for image-based point cloud generation from multi-camera systems. Similar to the automated aerial triangulation, this procedure also takes advantage of the prior trajectory information to facilitate the image-based 3D reconstruction process. Since both proposed procedures are based on a Structure-from-Motion (SfM) framework, they are capable of dealing with image-based 3D reconstruction in the absence of GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) information or only in the presence of less-accurate POS (Position Orientation System) information. Finally, an object space-based approach is proposed to achieve more efficient image-based dense point cloud generation. Experimental results from real datasets demonstrate the feasibility of the proposed procedures in providing accurate image-based 3D reconstruction from images acquired with different configurations.
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