Graph-based parallel large scale structure from motion
文献类型：期刊论文
| 作者 | Chen, Yu2; Shen, Shuhan1,3 ; Chen, Yisong2; Wang, Guoping2
|
| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2020-11-01 |
| 卷号 | 107页码:11 |
| 关键词 | Clustering Structure from motion Minimum spanning tree |
| ISSN号 | 0031-3203 |
| DOI | 10.1016/j.patcog.2020.107537 |
| 通讯作者 | Shen, Shuhan(shshen@nlpr.ia.ac.cn) |
| 英文摘要 | While Structure from Motion achieves great success in 3D reconstruction, it still meets challenges on large scale scenes. Incremental SfM approaches are robust to outliers, but are limited by low efficiency and easy suffer from drift problem. Though Global SfM methods are more efficient than incremental approaches, they are sensitive to outliers, and would also meet memory limitation and time bottleneck. In this work, large scale SfM is deemed as a graph problem, where graph are respectively constructed in image clustering step and local reconstructions merging step. By leveraging the graph structure, we are able to handle large scale dataset in divide-and-conquer manner. Firstly, images are modelled as graph nodes, with edges are retrieved from geometric information after feature matching. Then images are divided into independent clusters by a image clustering algorithm, and followed by a subgraph expansion step, the connection and completeness of scenes are enhanced by walking along a maximum spanning tree, which is utilized to construct overlapping images between clusters. Secondly, Image clusters are distributed into servers to execute SfM in parallel mode. Thirdly, after local reconstructions complete, we construct a minimum spanning tree to find accurate similarity transformations. Then the minimum spanning tree is transformed into a Minimum Height Tree to find a proper anchor node, and is further utilized to prevent error accumulation. We evaluate our approach on various kinds of datasets and our approach shows superiority over the state-of-the-art in accuracy and efficiency. Our algorithm is open-sourced in https://github.com/AIBluefisher/GraphSfM. (C) 2020 Elsevier Ltd. All rights reserved. |
| WOS关键词 | MODEL ; OPTIMIZATION ; ACCURATE ; CUTS |
| 资助项目 | National Key Technology Research and Development Program of China[2017YFB1002705] ; National Key Technology Research and Development Program of China[2017YFB0203002] ; National Key Technology Research and Development Program of China[2017YFB1002601] ; National Natural Science Foundation of China(NSFC)[61632003] ; National Natural Science Foundation of China(NSFC)[61661146002] ; National Natural Science Foundation of China(NSFC)[61872398] ; Equipment Development Project[315050501] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000552866000067 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | National Key Technology Research and Development Program of China ; National Natural Science Foundation of China(NSFC) ; Equipment Development Project |
| 源URL | [http://ir.ia.ac.cn/handle/173211/40286]  |
| 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 |
| 通讯作者 | Shen, Shuhan |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Peking Univ, Sch Elect Engn & Comp Sci, Dept Comp Sci & Technol, Graph & Interact Lab, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Yu,Shen, Shuhan,Chen, Yisong,et al. Graph-based parallel large scale structure from motion[J]. PATTERN RECOGNITION,2020,107:11. |
| APA | Chen, Yu,Shen, Shuhan,Chen, Yisong,&Wang, Guoping.(2020).Graph-based parallel large scale structure from motion.PATTERN RECOGNITION,107,11. |
| MLA | Chen, Yu,et al."Graph-based parallel large scale structure from motion".PATTERN RECOGNITION 107(2020):11. |
入库方式： OAI收割
来源：自动化研究所
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