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Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual–Real Integration

1
Department of Automation, University of Science and Technology of China, Hefei 230027, China
2
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3
Qingdao Academy of Intelligent Industries, Qingdao 266000, China
4
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100091, China
*
Author to whom correspondence should be addressed.
Academic Editor: Riccardo Roncella
Remote Sens. 2021, 13(15), 2868; https://doi.org/10.3390/rs13152868
Received: 21 May 2021 / Revised: 15 July 2021 / Accepted: 16 July 2021 / Published: 22 July 2021
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. View Full-Text
Keywords: virtual LiDAR; hybrid point clouds; virtual–real interaction; 3D detection virtual LiDAR; hybrid point clouds; virtual–real interaction; 3D detection
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MDPI and ACS Style

Tian, Y.; Wang, X.; Shen, Y.; Guo, Z.; Wang, Z.; Wang, F.-Y. Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual–Real Integration. Remote Sens. 2021, 13, 2868. https://doi.org/10.3390/rs13152868

AMA Style

Tian Y, Wang X, Shen Y, Guo Z, Wang Z, Wang F-Y. Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual–Real Integration. Remote Sensing. 2021; 13(15):2868. https://doi.org/10.3390/rs13152868

Chicago/Turabian Style

Tian, Yonglin, Xiao Wang, Yu Shen, Zhongzheng Guo, Zilei Wang, and Fei-Yue Wang. 2021. "Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual–Real Integration" Remote Sensing 13, no. 15: 2868. https://doi.org/10.3390/rs13152868

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