  The performance of modern object detectors drops when the test distribution
differs from the training one. Most of the methods that address this focus on
object appearance changes caused by, e.g., different illumination conditions,
or gaps between synthetic and real images. Here, by contrast, we tackle
geometric shifts emerging from variations in the image capture process, or due
to the constraints of the environment causing differences in the apparent
geometry of the content itself. We introduce a self-training approach that
learns a set of geometric transformations to minimize these shifts without
leveraging any labeled data in the new domain, nor any information about the
cameras. We evaluate our method on two different shifts, i.e., a camera's field
of view (FoV) change and a viewpoint change. Our results evidence that learning
geometric transformations helps detectors to perform better in the target
domains.
