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