中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 先进控制与自动化团队
Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual-Real Integration

文献类型：期刊论文

作者Tian, Yonglin1,2; Wang, Xiao2,3; Shen, Yu2,4; Guo, Zhongzheng2; Wang, Zilei1; Wang, Fei-Yue2,3
刊名REMOTE SENSING
出版日期2021-08-01
卷号13期号:15页码:17
关键词virtual LiDAR hybrid point clouds virtual-real interaction 3D detection
DOI10.3390/rs13152868
通讯作者Wang, Xiao(x.wang@ia.ac.cn)
英文摘要Three-dimensional information perception from point clouds is of vital importance for improving the ability of machines to understand the world, especially for autonomous driving and unmanned aerial vehicles. Data annotation for point clouds is one of the most challenging and costly tasks. In this paper, we propose a closed-loop and virtual-real interactive point cloud generation and model-upgrading framework called Parallel Point Clouds (PPCs). To our best knowledge, this is the first time that the training model has been changed from an open-loop to a closed-loop mechanism. The feedback from the evaluation results is used to update the training dataset, benefiting from the flexibility of artificial scenes. Under the framework, a point-based LiDAR simulation model is proposed, which greatly simplifies the scanning operation. Besides, a group-based placing method is put forward to integrate hybrid point clouds, via locating candidate positions for virtual objects in real scenes. Taking advantage of the CAD models and mobile LiDAR devices, two hybrid point cloud datasets, i.e., ShapeKITTI and MobilePointClouds, are built for 3D detection tasks. With almost zero labor cost on data annotation for newly added objects, the models (PointPillars) trained with ShapeKITTI and MobilePointClouds achieved 78.6% and 60.0% of the average precision of the model trained with real data on 3D detection, respectively.
WOS关键词SYSTEMS ; VISION
资助项目State Key Program of the National Natural Science Foundation of China[61533019] ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV) ; National Natural Science Foundation of China[U1811463] ; Key Research and Development Program of Guangzhou[202007050002]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者MDPI
WOS记录号WOS:000682162500001
资助机构State Key Program of the National Natural Science Foundation of China ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV) ; National Natural Science Foundation of China ; Key Research and Development Program of Guangzhou
源URL[http://ir.ia.ac.cn/handle/173211/45655]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Wang, Xiao
作者单位1.Univ Sci & Technol China, Dept Automat, Hefei 230027, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Qingdao Acad Intelligent Ind, Qingdao 266000, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100091, Peoples R China
推荐引用方式
GB/T 7714		 Tian, Yonglin,Wang, Xiao,Shen, Yu,et al. Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual-Real Integration[J]. REMOTE SENSING,2021,13(15):17.
APA Tian, Yonglin,Wang, Xiao,Shen, Yu,Guo, Zhongzheng,Wang, Zilei,&Wang, Fei-Yue.(2021).Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual-Real Integration.REMOTE SENSING,13(15),17.
MLA Tian, Yonglin,et al."Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual-Real Integration".REMOTE SENSING 13.15(2021):17.

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。