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Learning to refine depth for robust stereo estimation
2018-02
发表期刊PATTERN RECOGNITION (IF:7.5[JCR-2023],7.6[5-Year])
ISSN0031-3203
卷号74页码:122-133
发表状态已发表
DOI10.1016/j.patcog.2017.07.027
摘要Traditional depth estimation from stereo images is usually formulated as a patch-matching problem, which requires post-processing stages to impose smoothness and handle depth discontinuities and occlusions. While recent deep network approaches directly learn a regressor for the entire disparity map, they still suffer from large errors near the depth discontinuities. In this paper, we propose a novel method to refine the disparity maps generated by deep regression networks. Instead of relying on ad hoc post processing, we learn a unified deep network model that predicts a confidence map and the disparity gradients from the learned feature representation in regression networks. We integrate the initial disparity estimation, the confidence map and the disparity gradients into a continuous Markov Random Field (MRF) for depth refinement, which is capable of representing rich surface structures. Our disparity MRF model can be solved via efficient global optimization in a closed form. We evaluate our approach on both synthetic and real-world datasets, and the results show it achieves the state-of-art performance and produces more structure-preserving disparity maps with smaller errors in the neighborhood of depth boundaries. (C) 2017 Elsevier Ltd. All rights reserved.
关键词Stereo matching Confidence measure Convolutional neural network
收录类别SCI ; SCIE
语种英语
资助项目National Natural Science Foundation of China[61571026]
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000417547800010
出版者ELSEVIER SCI LTD
WOS关键词ACCURATE
原始文献类型Article
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文献类型期刊论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/14295
专题信息科学与技术学院_PI研究组_何旭明组
通讯作者Zhang, Hong
作者单位
1.Beihang Univ, Image Res Ctr, Xueyuan Rd, Beijing 100191, Peoples R China
2.Natl ICT Australia, Comp Vis Grp, Locked Bag 8001, Canberra, ACT 2601, Australia
3.Australia Natl Univ, Canberra, ACT, Australia
4.ShanghaiTech Univ, Shanghai, Peoples R China
推荐引用方式
GB/T 7714
Cheng, Feiyang,He, Xuming,Zhang, Hong. Learning to refine depth for robust stereo estimation[J]. PATTERN RECOGNITION,2018,74:122-133.
APA Cheng, Feiyang,He, Xuming,&Zhang, Hong.(2018).Learning to refine depth for robust stereo estimation.PATTERN RECOGNITION,74,122-133.
MLA Cheng, Feiyang,et al."Learning to refine depth for robust stereo estimation".PATTERN RECOGNITION 74(2018):122-133.
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