Knowledge Commons of Institute of Automation,CAS
Incremental Rotation Averaging | |
Gao, Xiang1; Zhu, Lingjie2,3; Xie, Zexiao1; Liu, Hongmin4; Shen, Shuhan2,3 | |
发表期刊 | International Journal of Computer Vision |
ISSN | 0920-5691 |
2021 | |
卷号 | 129页码:1202-1216 |
摘要 | In this paper, we present a simple yet effective rotation averaging pipeline, termed Incremental Rotation Averaging (IRA), which is inspired by the well-developed incremental Structure from Motion (SfM) techniques. Unlike the traditional rotation averaging methods which estimate all the absolute rotations simultaneously and focus on designing either robust loss function or outlier filtering strategy, here the absolute rotations are estimated in an incremental way. Similar to the incremental SfM, our IRA is robust to relative rotation outliers and could achieve accurate rotation averaging results. In addition, we propose several key techniques, such as initial triplet and Next-Best-View selection, Weighted Local/Global Optimization, and Re-Rotation Averaging, to push the rotation averaging results one step further. Ablation studies and comparison experiments on the 1DSfM, Campus, and San Francisco datasets demonstrate the effectiveness of our IRA and its advantages over the state-of-the-art rotation averaging methods in accuracy and robustness. |
关键词 | Rotation averaging Incremental estimation Accuracy and robustness |
DOI | 10.1007/s11263-020-01427-7 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2020YFB1313002] ; National Science Foundation of China[62003319] ; National Science Foundation of China[62076026] ; National Science Foundation of China[61873265] ; Shandong Provincial Natural Science Foundation[ZR2020QF075] ; China Postdoctoral Science Foundation[2020M682239] ; National Laboratory of Pattern Recognition[202000010] |
项目资助者 | National Key Research and Development Program of China ; National Science Foundation of China ; Shandong Provincial Natural Science Foundation ; China Postdoctoral Science Foundation ; National Laboratory of Pattern Recognition |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000608098500001 |
出版者 | SPRINGER |
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 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/42594 |
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.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Gao, Xiang,Zhu, Lingjie,Xie, Zexiao,et al. Incremental Rotation Averaging[J]. International Journal of Computer Vision,2021,129:1202-1216. |
APA | Gao, Xiang,Zhu, Lingjie,Xie, Zexiao,Liu, Hongmin,&Shen, Shuhan.(2021).Incremental Rotation Averaging.International Journal of Computer Vision,129,1202-1216. |
MLA | Gao, Xiang,et al."Incremental Rotation Averaging".International Journal of Computer Vision 129(2021):1202-1216. |
条目包含的文件 | 条目无相关文件。 |
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