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Automatic Generation of Synthetic LiDAR Point Clouds for 3-D Data Analysis

Wang, Fei and Zhuang, Yan and Gu, Hong and Hu, Huosheng (2019) 'Automatic Generation of Synthetic LiDAR Point Clouds for 3-D Data Analysis.' IEEE Transactions on Instrumentation and Measurement, 68 (7). 2671 - 2673. ISSN 0018-9456

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The recent success of deep learning in 3-D data analysis relies upon the availability of large annotated data sets. However, creating 3-D data sets with point-level labels are extremely challenging and require a huge amount of human efforts. This paper presents a novel open-sourced method to extract light detection and ranging point clouds with ground truth annotations from a simulator automatically. The virtual sensor can be configured to simulate various real devices, from 2-D laser scanners to 3-D real-time sensors. Experiments are conducted to show that using additional synthetic data for training can: 1) achieve a visible performance boost in accuracy; 2) reduce the amount of manually labeled real-world data; and 3) help to improve the generalization performance across data sets.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 25 Jun 2019 11:15
Last Modified: 25 Jun 2019 11:15

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