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Indexed by:期刊论文

Date of Publication:2019-07-01

Journal:IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

Included Journals:SCIE、EI

Volume:68

Issue:7

Page Number:2671-2673

ISSN No.:0018-9456

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

Abstract: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.