K. Pathak, N. Vaskevicius, Francisc Bungiu, A. Birk
{"title":"利用平面贴片匹配的三维扫描配准中的颜色信息","authors":"K. Pathak, N. Vaskevicius, Francisc Bungiu, A. Birk","doi":"10.1109/MFI.2012.6343047","DOIUrl":null,"url":null,"abstract":"In previous work, the authors presented a 3D scan-registration algorithm based on minimizing the uncertainty-volume of the estimated inter-scan transform, computed by matching planar-patches extracted from a pair of 3D range-images. The method was shown to have a larger region of convergence than points-based methods like ICP. With the advent of newer sensors, color-information is now also available in addition to the depth-information in range-images. In this work, we show how this information can be exploited to make our algorithm computationally more efficient. The results are presented for two commercially available sensors providing color: the high-resolution, large field-of-view (FOV), slow scanning Faro sensor, and the low-resolution, small FOV, faster Kinect sensor.","PeriodicalId":103145,"journal":{"name":"2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"30 8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Utilizing color information in 3D scan-registration using planar-patches matching\",\"authors\":\"K. Pathak, N. Vaskevicius, Francisc Bungiu, A. Birk\",\"doi\":\"10.1109/MFI.2012.6343047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In previous work, the authors presented a 3D scan-registration algorithm based on minimizing the uncertainty-volume of the estimated inter-scan transform, computed by matching planar-patches extracted from a pair of 3D range-images. The method was shown to have a larger region of convergence than points-based methods like ICP. With the advent of newer sensors, color-information is now also available in addition to the depth-information in range-images. In this work, we show how this information can be exploited to make our algorithm computationally more efficient. The results are presented for two commercially available sensors providing color: the high-resolution, large field-of-view (FOV), slow scanning Faro sensor, and the low-resolution, small FOV, faster Kinect sensor.\",\"PeriodicalId\":103145,\"journal\":{\"name\":\"2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)\",\"volume\":\"30 8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MFI.2012.6343047\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MFI.2012.6343047","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Utilizing color information in 3D scan-registration using planar-patches matching
In previous work, the authors presented a 3D scan-registration algorithm based on minimizing the uncertainty-volume of the estimated inter-scan transform, computed by matching planar-patches extracted from a pair of 3D range-images. The method was shown to have a larger region of convergence than points-based methods like ICP. With the advent of newer sensors, color-information is now also available in addition to the depth-information in range-images. In this work, we show how this information can be exploited to make our algorithm computationally more efficient. The results are presented for two commercially available sensors providing color: the high-resolution, large field-of-view (FOV), slow scanning Faro sensor, and the low-resolution, small FOV, faster Kinect sensor.