LiDAR Point Cloud Augmentation for Adverse Conditions Using Conditional Generative Model

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 12; p. 2247
• Main Authors: Zhang, Yuxiao, Ding, Ming, Yang, Hanting, Niu, Yingjie, Ge, Maoning, Ohtani, Kento, Zhang, Chi, Takeda, Kazuya
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Accuracy; adverse conditions; Algorithms; Background noise; Clouds; conditional generative model; Data augmentation; Datasets; Deep learning; Depth perception; Driving; Fog; Lidar; Machine learning; Meteorological data; Object recognition; Perception; point cloud processing; Precipitation; Rain; Sensors; Side effects; Snow; System reliability; Weather

The perception systems of autonomous vehicles face significant challenges under adverse conditions, with issues such as obscured objects and false detections due to environmental noise. Traditional approaches, which typically focus on noise removal, often fall short in such scenarios. Addressing the...