Efficient 3D object recognition via geometric information preservation
文献类型：期刊论文
| 作者 | Yang, Chenguang1; Liu HS(刘洪森)2,3,4 ; Cong Y(丛杨)2,4; Tang YD(唐延东)2,4
|
| 刊名 | Pattern Recognition
 |
| 出版日期 | 2019 |
| 卷号 | 92页码:135-145 |
| 关键词 | Stacked 3D feature encoder 3D object recognition 6-DOF pose estimation Geometric information preservation |
| ISSN号 | 0031-3203 |
| 产权排序 | 1 |
| 英文摘要 | Accurate 3D object recognition and 6-DOF pose estimation have been pervasively applied to a variety of applications, such as unmanned warehouse, cooperative robots, and manufacturing industry. How to extract a robust and representative feature from the point clouds is an inevitable and important issue. In this paper, an unsupervised feature learning network is introduced to extract 3D keypoint features from point clouds directly, rather than transforming point clouds to voxel grids or projected RGB images, which saves computational time while preserving the object geometric information as well. Specifically, the proposed network features in a stacked point feature encoder, which can stack the local discriminative features within its neighborhoods to the original point-wise feature counterparts. The main framework consists of both offline training phase and online testing phase. In the offline training phase, the stacked point feature encoder is trained first and then generate feature database of all keypoints, which are sampled from synthetic point clouds of multiple model views. In the online testing phase, each feature extracted from the unknown testing scene is matched among the database by using the K-D tree voting strategy. Afterwards, the matching results are achieved by using the hypothesis & verification strategy. The proposed method is extensively evaluated on four public datasets and the results show that ours deliver comparable or even superior performances than the state-of-the-arts in terms of F1-score, Average of the 3D distance (ADD) and Recognition rate. |
| 语种 | 英语 |
| 资助机构 | Nature Science Foundation of China under Grant (61722311, U1613214, 61821005, 61533015) ; CAS-Youth Innovation Promotion Association Scholarship (2012163) |
| 源URL | [http://ir.sia.cn/handle/173321/24474]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Cong Y(丛杨) |
| 作者单位 | 1.Bristol Robotics Laboratory, University of the West of England, Bristol, BS16 1QY, United Kingdom 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China 3.University of Chinese Academy of Sciences, 100049, China 4.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China |
| 推荐引用方式 GB/T 7714 | Yang, Chenguang,Liu HS,Cong Y,et al. Efficient 3D object recognition via geometric information preservation[J]. Pattern Recognition,2019,92:135-145. |
| APA | Yang, Chenguang,Liu HS,Cong Y,&Tang YD.(2019).Efficient 3D object recognition via geometric information preservation.Pattern Recognition,92,135-145. |
| MLA | Yang, Chenguang,et al."Efficient 3D object recognition via geometric information preservation".Pattern Recognition 92(2019):135-145. |
入库方式： OAI收割
来源：沈阳自动化研究所
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。