  Closing the domain gap between training and deployment and incorporating
multiple sensor modalities are two challenging yet critical topics for
self-driving. Existing work only focuses on single one of the above topics,
overlooking the simultaneous domain and modality shift which pervasively exists
in real-world scenarios. A model trained with multi-sensor data collected in
Europe may need to run in Asia with a subset of input sensors available. In
this work, we propose DualCross, a cross-modality cross-domain adaptation
framework to facilitate the learning of a more robust monocular bird's-eye-view
(BEV) perception model, which transfers the point cloud knowledge from a LiDAR
sensor in one domain during the training phase to the camera-only testing
scenario in a different domain. This work results in the first open analysis of
cross-domain cross-sensor perception and adaptation for monocular 3D tasks in
the wild. We benchmark our approach on large-scale datasets under a wide range
of domain shifts and show state-of-the-art results against various baselines.
