  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 tremendous
potential to complement real data, due to their scalability and high-fidelity
compared to graphics engine based methods. Before simulation can be deployed in
the real-world, two shortcomings need to be addressed. First, existing methods
usually generate data which are more noisy and complete than the real point
clouds, due to 3D reconstruction error and pure geometry-based raycasting
method. Second, prior works on simulation for object detection focus solely on
rigid objects, like cars, but VRUs, like pedestrians, are important road
participants. To tackle the first challenge, we propose FPA raycasting and
surrogate model raydrop. FPA enables the simulation of both point cloud
coordinates and sensor features, while taking into account reconstruction
noise. The ray-wise surrogate raydrop model mimics the physical properties of
LiDAR's laser receiver to determine whether a simulated point would be recorded
by a real LiDAR. With minimal training data, the surrogate model can generalize
to different geographies and scenes, closing the domain gap between raycasted
and real point clouds. To tackle the simulation of deformable VRU simulation,
we employ SMPL dataset to provide a pedestrian simulation baseline and compare
the domain gap between CAD and reconstructed objects. Applying our pipeline to
perform novel sensor synthesis, results show that object detection models
trained by simulation data can achieve similar result as the real data trained
model.
