Simulating LIDAR Point Cloud for Autonomous Driving using Real-world Scenes and Traffic Flows1/19/2024 We verify the effectiveness of our NeRF-LiDAR by trainingĭifferent 3D segmentation models on the generated LiDAR point clouds. Self-driving cars to learn the 3D scene representation, point cloud generationĪnd label rendering. Different fromĮxisting LiDAR simulators, we use real images and point cloud data collected by Real-world information to generate realistic LIDAR point clouds. This, we present NeRF-LIDAR, a novel LiDAR simulation method that leverages Novel view synthesis using implicit reconstruction of 3D scenes. Recently, Neural Radiance Fields (NeRF) have been proposed for LiDAR simulation aims at generating realistic LiDARĭata with labels for training and verifying self-driving algorithms moreĮfficiently. Download a PDF of the paper titled NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields, by Junge Zhang and 3 other authors Download PDF Abstract: Labeling LiDAR point clouds for training autonomous driving is extremelyĮxpensive and difficult.
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