  Domain shifts such as sensor type changes and geographical situation
variations are prevalent in Autonomous Driving (AD), which poses a challenge
since AD model relying on the previous domain knowledge can be hardly directly
deployed to a new domain without additional costs. In this paper, we provide a
new perspective and approach of alleviating the domain shifts, by proposing a
Reconstruction-Simulation-Perception (ReSimAD) scheme. Specifically, the
implicit reconstruction process is based on the knowledge from the previous old
domain, aiming to convert the domain-related knowledge into domain-invariant
representations, e.g., 3D scene-level meshes. Besides, the point clouds
simulation process of multiple new domains is conditioned on the above
reconstructed 3D meshes, where the target-domain-like simulation samples can be
obtained, thus reducing the cost of collecting and annotating new-domain data
for the subsequent perception process. For experiments, we consider different
cross-domain situations such as Waymo-to-KITTI, Waymo-to-nuScenes,
Waymo-to-ONCE, etc, to verify the zero-shot target-domain perception using
ReSimAD. Results demonstrate that our method is beneficial to boost the domain
generalization ability, even promising for 3D pre-training.
