{"title":"Dense depth recovery based on adaptive image segmentation","authors":"Shangli Liang, C. Yuan","doi":"10.1109/ICCE-CHINA.2013.6780863","DOIUrl":null,"url":null,"abstract":"Stereoscopic vision has been a hot research topic in recent years. As one of the most important steps for 3D reconstruction from image sequences, dense depth recovery has attracted much attention in computer vision recently. A lot of efficient algorithms based on stereo matching have been proposed. But the difficulties in depth recovery are not overcome completely yet. In this paper, we aim at the main problems of depth recovery and propose our solutions. And an effective approach for dense depth recovery based on adaptive image segmentation is also presented. Experiments show that accurate depth results can be achieved through the proposed approach.","PeriodicalId":293342,"journal":{"name":"2013 IEEE International Conference on Consumer Electronics - China","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Consumer Electronics - China","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCE-CHINA.2013.6780863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Stereoscopic vision has been a hot research topic in recent years. As one of the most important steps for 3D reconstruction from image sequences, dense depth recovery has attracted much attention in computer vision recently. A lot of efficient algorithms based on stereo matching have been proposed. But the difficulties in depth recovery are not overcome completely yet. In this paper, we aim at the main problems of depth recovery and propose our solutions. And an effective approach for dense depth recovery based on adaptive image segmentation is also presented. Experiments show that accurate depth results can be achieved through the proposed approach.