서지주요정보
Depth estimation using sequential information for online 3D reconstruction = 온라인 3차원 복원을 위한 순차적 정보 기반의 깊이 추정
서명 / 저자 Depth estimation using sequential information for online 3D reconstruction = 온라인 3차원 복원을 위한 순차적 정보 기반의 깊이 추정 / Truong Giang Khang.
발행사항 [대전 : 한국과학기술원, 2021].
Online Access 원문보기 원문인쇄

소장정보

등록번호

8038018

소장위치/청구기호

학술문화관(도서관)2층 패컬티라운지(학위논문)

MCS 21054

휴대폰 전송

도서상태

이용가능(대출불가)

사유안내

반납예정일

리뷰정보

초록정보

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.

서지기타정보

서지기타정보
청구기호 {MCS 21054
형태사항 v, 40 p. : 삽화 ; 30 cm
언어 영어
일반주기 저자명의 한글표기 : Khang Truong Giang
지도교수의 영문표기 : Sungho Jo
지도교수의 한글표기 : 조성호
수록잡지명 : "Sequential depth completion with confidence estimation for 3D model reconstruction". IEEE Robotics and Automation Letters, v.6.no.2, pp.327-334(2020)
Including Appendix
학위논문 학위논문(석사) - 한국과학기술원 : 전산학부,
서지주기 References : p. 34-38
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