Confidence-Based Large-Scale Dense Multi-View Stereo

doi:10.1109/TIP.2020.2999853

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Confidence-Based Large-Scale Dense Multi-View Stereo
	Li, Zhaoxin 1,2; Zuo, Wangmeng 3; Wang, Zhaoqi 2; Zhang, Lei 1
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2020
卷号	29 页码:7176-7191
关键词	Multi-view stereo confidence large-scale interpolation static and dynamic guidance refinement
ISSN号	1057-7149
DOI	10.1109/TIP.2020.2999853
英文摘要	Albeit remarkable progress has been made to improve the accuracy and completeness of multi-view stereo (MVS), existing methods still suffer from either sparse reconstructions of low-textured surfaces or heavy computational burden. In this paper, we propose a Confidence-based Large-scale Dense Multi-view Stereo (CLD-MVS) method for high resolution imagery. Firstly, we formulate MVS as a multi-view depth estimation problem, and employ a normal-aware efficient PatchMatch stereo to estimate the initial depth and normal map for each reference view. A self-supervised deep learning method is then developed to predict the spatial confidence for multi-view depth maps, which is combined with cross-view consistency to generate the ground control points. Subsequently, a confidence-driven and boundary-aware interpolation scheme using static and dynamic guidance is adopted to synthesize dense depth and normal maps. Finally, a refinement procedure which leverages synthesized depth and normal as prior is conducted to estimate cross-view consistent surface. Experiments show that the proposed CLD-MVS method achieves high geometric completeness while preserving fine-scale details. In particular, it has ranked No. 1 on the ETH3D high-resolution MVS benchmark in terms of F-1-score.
资助项目	NVIDIA Corporation
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000546910100021
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/15088]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Zhaoxin
作者单位	1.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China
推荐引用方式 GB/T 7714	Li, Zhaoxin,Zuo, Wangmeng,Wang, Zhaoqi,et al. Confidence-Based Large-Scale Dense Multi-View Stereo[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29:7176-7191.
APA	Li, Zhaoxin,Zuo, Wangmeng,Wang, Zhaoqi,&Zhang, Lei.(2020).Confidence-Based Large-Scale Dense Multi-View Stereo.IEEE TRANSACTIONS ON IMAGE PROCESSING,29,7176-7191.
MLA	Li, Zhaoxin,et al."Confidence-Based Large-Scale Dense Multi-View Stereo".IEEE TRANSACTIONS ON IMAGE PROCESSING 29(2020):7176-7191.