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 Jiun | en_US |
dc.creator (作者) | 蔡政君 | zh_TW |
dc.creator (作者) | Chan, Kai-Hsuan | en_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) | article | en |
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 | - |