ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation
ICLR 2024(2023)
摘要
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.
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关键词
Autonomous Driving,3D Domain Transfer,Zero-shot 3D Detection
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