{"title":"Robust range image registration using 3D lines","authors":"Jian Yao, M. Ruggeri, P. Taddei, V. Sequeira","doi":"10.1109/ICIP.2010.5652449","DOIUrl":null,"url":null,"abstract":"We present an efficient method for accurate automatic registration of two geometrically complex 3D range scans by using 3D lines. We first detect edges from the associated 2D reflectance images and collect 3D edge contours by only taking into account valid foreground points. Then we use an efficient split-and-merge line fitting algorithm to detect 3D lines. We build a fast search codebook to efficiently match the two sets of 3D lines. This is done by computing the orientation angle and distance of pairs of 3D lines in each set, both of which are invariant under rigid transformations. Finally we recover the rigid transformation between two scans using an efficient RANSAC algorithm with robust transformation estimation that exploits two sets of corresponding 3D lines. We conclude presenting experimental results that demonstrate efficiency and accuracy of our proposed method.","PeriodicalId":228308,"journal":{"name":"2010 IEEE International Conference on Image Processing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.2010.5652449","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
We present an efficient method for accurate automatic registration of two geometrically complex 3D range scans by using 3D lines. We first detect edges from the associated 2D reflectance images and collect 3D edge contours by only taking into account valid foreground points. Then we use an efficient split-and-merge line fitting algorithm to detect 3D lines. We build a fast search codebook to efficiently match the two sets of 3D lines. This is done by computing the orientation angle and distance of pairs of 3D lines in each set, both of which are invariant under rigid transformations. Finally we recover the rigid transformation between two scans using an efficient RANSAC algorithm with robust transformation estimation that exploits two sets of corresponding 3D lines. We conclude presenting experimental results that demonstrate efficiency and accuracy of our proposed method.