Incremental Translation Averaging
文献类型：期刊论文
| 作者 | Gao, Xiang5,6 ; Zhu, Lingjie4; Fan, Bin3; Liu, Hongmin3; Shen, Shuhan1,2,5
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| 刊名 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
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| 出版日期 | 2022-11-01 |
| 卷号 | 32期号:11页码:7783-7795 |
| ISSN号 | 1051-8215 |
| 关键词 | Cameras Estimation Robustness Pipelines Cost function Parameter estimation Barium Translation averaging incremental estimation accuracy and robustness simplicity and efficiency |
| DOI | 10.1109/TCSVT.2022.3183631 |
| 通讯作者 | Liu, Hongmin(hmliu_82@163.com) ; Shen, Shuhan(shshen@nlpr.ia.ac.cn) |
| 英文摘要 | Translation averaging is known to be more difficult than rotation averaging due to scale ambiguity, estimation sensitivity, and solution uncertainty. Existing approaches have exposed their limitations in terms of accuracy, robustness, simplicity, or efficiency. To tackle this tough problem, a simple yet effective translation averaging pipeline, termed as Incremental Translation Averaging (ITA), is proposed in this paper. It combines the advantages of high accuracy and robustness in incremental parameter estimation pipeline and the advantages of high simplicity and efficiency in global motion averaging approach. Unlike the traditional translation averaging methods which estimate all the absolute camera locations simultaneously and suffer from inaccuracy in parameter estimation and incompleteness in scene reconstruction, our ITA computes them novelly in an incremental way with higher accuracy and robustness. Thanks to the introduction of incremental parameter estimation thought into the translation averaging pipeline, 1) our ITA is robust to measurement outliers and accurate in parameter estimation; and 2) our ITA is simple and efficient because of its less dependency on complicated optimization, carefully-designed preprocessing, or additional information. Comprehensive evaluations on the 1DSfM dataset demonstrate the effectiveness of our ITA and its advantages over several state-of-the-art translation averaging approaches. |
| WOS关键词 | EFFICIENT ; MOTION |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000876020600039 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/50696]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_机器人视觉团队 中科院工业视觉智能装备工程实验室 |
| 通讯作者 | Liu, Hongmin; Shen, Shuhan |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 3.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China 4.Alibaba AI Labs, Hangzhou 311121, Peoples R China 5.CASIA SenseTime Res Grp, Beijing 100190, Peoples R China 6.Ocean Univ China, Coll Engn, Qingdao 266100, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Xiang,Zhu, Lingjie,Fan, Bin,et al. Incremental Translation Averaging[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2022,32(11):7783-7795. |
| APA | Gao, Xiang,Zhu, Lingjie,Fan, Bin,Liu, Hongmin,&Shen, Shuhan.(2022).Incremental Translation Averaging.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,32(11),7783-7795. |
| MLA | Gao, Xiang,et al."Incremental Translation Averaging".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 32.11(2022):7783-7795. |
入库方式： OAI收割
来源：自动化研究所
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