Knowledge Commons of Institute of Automation,CAS
Incremental Translation Averaging | |
Gao, Xiang1,2; Zhu, Lingjie3; Fan, Bin4; Liu, Hongmin4; Shen, Shuhan2,5,6 | |
发表期刊 | IEEE Transactions on Circuits and Systems for Video Technology |
ISSN | 1051-8215 |
2022 | |
卷号 | 32期号:11页码:7783-7795 |
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. |
关键词 | Translation averaging Incremental estimation Accuracy and robustness Simplicity and efficiency |
DOI | 10.1109/TCSVT.2022.3183631 |
关键词[WOS] | EFFICIENT ; MOTION |
收录类别 | SCI |
语种 | 英语 |
WOS研究方向 | Engineering |
WOS类目 | Engineering, Electrical & Electronic |
WOS记录号 | WOS:000876020600039 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 三维视觉 |
国重实验室规划方向分类 | 环境多维感知 |
是否有论文关联数据集需要存交 | 是 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/50697 |
专题 | 多模态人工智能系统全国重点实验室_机器人视觉 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.Ocean Univ China, Coll Engn, Qingdao 266100, Peoples R China 2.CASIA SenseTime Res Grp, Beijing 100190, Peoples R China 3.Alibaba AI Labs, Hangzhou 311121, Peoples R China 4.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China 5.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所; 模式识别国家重点实验室 |
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. |
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