{"title":"连续空间的亚像素视差估计","authors":"Li-De Chen, Jo-Jiun Yu, Wei-Han Cheng, Chao-Tsung Huang","doi":"10.1109/ICCE-TW.2015.7216807","DOIUrl":null,"url":null,"abstract":"For depth-from-stereo vision applications such as driving assistance and object-size estimation, the accuracy of disparity estimation determines the precision of depth measurement. Conventional dense methods are hard to estimate disparity within a 0.1 pixel precision. In this paper, we present a novel object-based method to achieve robust and deep sub-pixel accurate disparity estimation. In our experimental system, it can provide depth measurement with less than 5% relative error within 150 m.","PeriodicalId":340402,"journal":{"name":"2015 IEEE International Conference on Consumer Electronics - Taiwan","volume":"120 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sub-pixel disparity estimation in continuous space\",\"authors\":\"Li-De Chen, Jo-Jiun Yu, Wei-Han Cheng, Chao-Tsung Huang\",\"doi\":\"10.1109/ICCE-TW.2015.7216807\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For depth-from-stereo vision applications such as driving assistance and object-size estimation, the accuracy of disparity estimation determines the precision of depth measurement. Conventional dense methods are hard to estimate disparity within a 0.1 pixel precision. In this paper, we present a novel object-based method to achieve robust and deep sub-pixel accurate disparity estimation. In our experimental system, it can provide depth measurement with less than 5% relative error within 150 m.\",\"PeriodicalId\":340402,\"journal\":{\"name\":\"2015 IEEE International Conference on Consumer Electronics - Taiwan\",\"volume\":\"120 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Consumer Electronics - Taiwan\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCE-TW.2015.7216807\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Consumer Electronics - Taiwan","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCE-TW.2015.7216807","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sub-pixel disparity estimation in continuous space
For depth-from-stereo vision applications such as driving assistance and object-size estimation, the accuracy of disparity estimation determines the precision of depth measurement. Conventional dense methods are hard to estimate disparity within a 0.1 pixel precision. In this paper, we present a novel object-based method to achieve robust and deep sub-pixel accurate disparity estimation. In our experimental system, it can provide depth measurement with less than 5% relative error within 150 m.
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