Depth estimation using sequential information for online 3D reconstruction온라인 3차원 복원을 위한 순차적 정보 기반의 깊이 추정

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We address the depth estimation problem in order to achieve a high-quality 3D reconstruction of the scene. In this thesis, we focus on two major problems of depth estimation including depth completion from sparse measurements and depth prediction in multi-view stereo. We propose novel deep learning architectures for these problems. Our proposals are mainly based on the sequential property of the input such as video or a sequence of images. They utilize the estimated depths of neighboring views to compensate estimation of the reference view. As a result, the proposed methods can produce temporal consistent depth. We also propose an uncertainty estimation for the predicted depth to efficiently remove the outliers when performing 3D reconstruction. Extensive experiments show that our frameworks achieve significant improvement compared with the state-of-the-art baselines in both offline and online fashion.
Advisors
Jo, Sunghoresearcher조성호researcher
Description
한국과학기술원 :전산학부,
Publisher
한국과학기술원
Issue Date
2021
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전산학부, 2021.8,[v, 40 p. :]

Keywords

Depth completion▼aDepth prediction▼aMulti-view stereo▼aDepth uncertainty▼a3D reconstruction

URI
http://hdl.handle.net/10203/296086
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=963358&flag=dissertation
Appears in Collection
CS-Theses_Master(석사논문)
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