12/5/2023 Simulating LIDAR Point Cloud for Autonomous Driving using Real-world Scenes and Traffic FlowsRead NowPoints) with these trained with real data. Simulated LiDAR point cloud alone can perform comparably (within two percentage In this paper, weĭescribe our simulator in detail, in particular the placement of obstacles that Practical, ready for large-scale industrial applications. Unique "scan-and-simulate" capability makes our approach scalable and Scanner to sweep the street of interests to obtain the background point cloud,īased on which annotated point cloud can be automatically generated. Instead, we can simply deploy a vehicle with a LiDAR Our augmented simulator bypasses the requirement to create high-fidelityīackground CAD models. Unlike previous simulators that entirely rely on CG models and game engines, Synthetic obstacles (e.g., cars, pedestrians, and other movable objects). Paper, we propose a novel LiDAR simulator that augments real point cloud with Point cloud is a very challenging, time- and money-consuming task. This experiment demonstrates that extending the costly real training data by easier accessible simulated point clouds improves the segmentation accuracy. Deep-learning based methods using annotated LiDAR data haveīeen the most widely adopted approach for this. Download a PDF of the paper titled Augmented LiDAR Simulator for Autonomous Driving, by Jin Fang and 6 other authors Download PDF Abstract: In Autonomous Driving (AD), detection and tracking of obstacles on the roads
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