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Annotating objects with 3D bounding boxes in LiDAR pointclouds is a costlyhuman driven process in an autonomous driving perception system. In this paper,we present a method to semi-automatically annotate real-world pointcloudscollected by deployment vehicles using simulated data. We train a 3D objectdetector model on labeled simulated data from CARLA jointly with real worldpointclouds from our target vehicle. The supervised object detection loss isaugmented with a CORAL loss term to reduce the distance between labeledsimulated and unlabeled real pointcloud feature representations. The goal hereis to learn representations that are invariant to simulated (labeled) andreal-world (unlabeled) target domains. We also provide an updated survey ondomain adaptation methods for pointclouds.

Simulation-to-Reality domain adaptation for offline 3D object annotation on pointclouds with correlation alignment