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Data is a fundamental building block for LiDAR perception systems.Unfortunately, real-world data collection and annotation is extremely costly &laborious. Recently, real data based LiDAR simulators have shown tremendouspotential to complement real data, due to their scalability and high-fidelitycompared to graphics engine based methods. Before simulation can be deployed inthe real-world, two shortcomings need to be addressed. First, existing methodsusually generate data which are more noisy and complete than the real pointclouds, due to 3D reconstruction error and pure geometry-based raycastingmethod. Second, prior works on simulation for object detection focus solely onrigid objects, like cars, but VRUs, like pedestrians, are important roadparticipants. To tackle the first challenge, we propose FPA raycasting andsurrogate model raydrop. FPA enables the simulation of both point cloudcoordinates and sensor features, while taking into account reconstructionnoise. The ray-wise surrogate raydrop model mimics the physical properties ofLiDAR's laser receiver to determine whether a simulated point would be recordedby a real LiDAR. With minimal training data, the surrogate model can generalizeto different geographies and scenes, closing the domain gap between raycastedand real point clouds. To tackle the simulation of deformable VRU simulation,we employ SMPL dataset to provide a pedestrian simulation baseline and comparethe domain gap between CAD and reconstructed objects. Applying our pipeline toperform novel sensor synthesis, results show that object detection modelstrained by simulation data can achieve similar result as the real data trainedmodel.

PCGen: Point Cloud Generator for LiDAR Simulation