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论文类型：期刊论文

发表时间：2019-07-01

发表刊物：IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

收录刊物：SCIE、EI

卷号：68

期号：7

页面范围：2671-2673

ISSN号：0018-9456

关键字：Deep learning; semantic segmentation; synthetic light detection and ranging (LiDAR) point clouds

摘要：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.