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題名 Robust refinement methods for camera calibration and 3D reconstruction from multiple images
作者 Hor, Maw-Kae
何瑁鎧
Tang, C.-Y.
詹凱軒
Tsai, Jeng Jiun
蔡政君
Chan, Kai-Hsuan
Wu, Y.-L.
貢獻者 資科系
關鍵詞 3D reconstruction; Bundle adjustments; Camera calibration; Outlier; Refinement; Algorithms; Calibration; Cameras; Three dimensional
日期 2011-06
上傳時間 22-Jun-2015 16:13:53 (UTC+8)
摘要 This paper proposes robust refinement methods to improve the popular patch multi-view 3D reconstruction algorithm by Furukawa and Ponce (2008). Specifically, a new method is proposed to improve the robustness by removing outliers based on a filtering approach. In addition, this work also proposes a method to divide the 3D points in to several buckets for applying the sparse bundle adjustment algorithm (SBA) individually, removing the outliers and finally merging them. The residuals are used to filter potential outliers to reduce the re-projection error used as the performance evaluation of refinement. In our experiments, the original mean re-projection error is about 47.6. After applying the proposed methods, the mean error is reduced to 2.13. © 2011 Elsevier B.V. All rights reserved.
關聯 Pattern Recognition Letters, 32(8), 1210-1221
資料類型 article
DOI http://dx.doi.org/10.1016/j.patrec.2011.03.007
dc.contributor 資科系-
dc.creator (作者) Hor, Maw-Kae-
dc.creator (作者) 何瑁鎧zh_TW
dc.creator (作者) Tang, C.-Y.en_US
dc.creator (作者) 詹凱軒zh_TW
dc.creator (作者) Tsai, Jeng Jiunen_US
dc.creator (作者) 蔡政君zh_TW
dc.creator (作者) Chan, Kai-Hsuanen_US
dc.creator (作者) Wu, Y.-L.en_US
dc.date (日期) 2011-06-
dc.date.accessioned 22-Jun-2015 16:13:53 (UTC+8)-
dc.date.available 22-Jun-2015 16:13:53 (UTC+8)-
dc.date.issued (上傳時間) 22-Jun-2015 16:13:53 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/76056-
dc.description.abstract (摘要) This paper proposes robust refinement methods to improve the popular patch multi-view 3D reconstruction algorithm by Furukawa and Ponce (2008). Specifically, a new method is proposed to improve the robustness by removing outliers based on a filtering approach. In addition, this work also proposes a method to divide the 3D points in to several buckets for applying the sparse bundle adjustment algorithm (SBA) individually, removing the outliers and finally merging them. The residuals are used to filter potential outliers to reduce the re-projection error used as the performance evaluation of refinement. In our experiments, the original mean re-projection error is about 47.6. After applying the proposed methods, the mean error is reduced to 2.13. © 2011 Elsevier B.V. All rights reserved.-
dc.format.extent 176 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Pattern Recognition Letters, 32(8), 1210-1221-
dc.subject (關鍵詞) 3D reconstruction; Bundle adjustments; Camera calibration; Outlier; Refinement; Algorithms; Calibration; Cameras; Three dimensional-
dc.title (題名) Robust refinement methods for camera calibration and 3D reconstruction from multiple images-
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1016/j.patrec.2011.03.007-
dc.doi.uri (DOI) http://dx.doi.org/10.1016/j.patrec.2011.03.007-