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政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 > Item 140.119/76056

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/76056

Title:	Robust refinement methods for camera calibration and 3D reconstruction from multiple images
Authors:	Hor, Maw-Kae 何瑁鎧 Tang, C.-Y. 詹凱軒 Tsai, Jeng Jiun 蔡政君 Chan, Kai-Hsuan Wu, Y.-L.
Contributors:	資科系
Keywords:	3D reconstruction;Bundle adjustments;Camera calibration;Outlier;Refinement;Algorithms;Calibration;Cameras;Three dimensional
Date:	2011-06
Issue Date:	2015-06-22 16:13:53 (UTC+8)
Abstract:	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.
Relation:	Pattern Recognition Letters, 32(8), 1210-1221
Data Type:	article
DOI 連結:	http://dx.doi.org/10.1016/j.patrec.2011.03.007
DOI:	10.1016/j.patrec.2011.03.007
Appears in Collections:	[資訊科學系] 期刊論文

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