Accurate and robust registration of low overlapping point clouds
文献类型：期刊论文
| 作者 | Yang, Jieyin3,4; Zhao, Mingyang1,2; Wu, Yingrui1,3 ; Jia, Xiaohong3,4
|
| 刊名 | COMPUTERS & GRAPHICS-UK
 |
| 出版日期 | 2024-02-01 |
| 卷号 | 118页码:146-160 |
| ISSN号 | 0097-8493 |
| 关键词 | Point cloud registration ICP HMRF Low overlapping Outliers |
| DOI | 10.1016/j.cag.2023.12.003 |
| 通讯作者 | Jia, Xiaohong(xhjia@amss.ac.cn) |
| 英文摘要 | Point cloud registration has various applications within the computer -aided design (CAD) community, such as model reconstruction, retrieving, and analysis. Previous approaches mainly deal with the registration with a high overlapping hypothesis, while few existing methods explore the registration between low overlapping point clouds. However, the latter registration task is both challenging and essential, since the weak correspondence in point clouds usually leads to an inappropriate initialization, making the algorithm get stuck in a local minimum. To improve the performance against low overlapping scenarios, in this work, we develop a novel algorithm for accurate and robust registration of low overlapping point clouds using optimal transformation. The core of our method is the effective integration of geometric features with the probabilistic model hidden Markov random field. First, we determine and remove the outliers of the point clouds by modeling a hidden Markov random field based on a high dimensional feature distribution. Then, we derive a necessary and sufficient condition when the symmetric function is minimized and present a new curvature -aware symmetric function to make the point correspondence more discriminative. Finally, we integrate our curvature -aware symmetric function into a geometrically stable sampling framework, which effectively constrains unstable transformations. We verify the accuracy and robustness of our method on a wide variety of datasets, particularly on low overlapping range scanned point clouds. Results demonstrate that our proposed method attains better performance with higher accuracy and robustness compared to representative state-of-the-art approaches. |
| WOS关键词 | SAMPLE CONSENSUS ; PARAMETERS |
| 资助项目 | National Key R&D Program of China[2021YFB1715900] ; National Natural Science Foundation of China[12022117] ; CAS Project for Young Scientists in Basic Research[YSBR-034] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| WOS记录号 | WOS:001152097000001 |
| 资助机构 | National Key R&D Program of China ; National Natural Science Foundation of China ; CAS Project for Young Scientists in Basic Research |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55410]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Jia, Xiaohong |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing, Peoples R China 2.Chinese Acad Sci, CAIR Hong Kong Inst Sci & Innovat, Hong Kong, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Chinese Acad Sci, Acad Math & Syst Sci, KLMM, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Jieyin,Zhao, Mingyang,Wu, Yingrui,et al. Accurate and robust registration of low overlapping point clouds[J]. COMPUTERS & GRAPHICS-UK,2024,118:146-160. |
| APA | Yang, Jieyin,Zhao, Mingyang,Wu, Yingrui,&Jia, Xiaohong.(2024).Accurate and robust registration of low overlapping point clouds.COMPUTERS & GRAPHICS-UK,118,146-160. |
| MLA | Yang, Jieyin,et al."Accurate and robust registration of low overlapping point clouds".COMPUTERS & GRAPHICS-UK 118(2024):146-160. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。