  Existing monocular 3D object detection methods have been demonstrated on
rectilinear perspective images and fail in images with alternative projections
such as those acquired by fisheye cameras. Previous works on object detection
in fisheye images have focused on 2D object detection, partly due to the lack
of 3D datasets of such images. In this work, we show how to use existing
monocular 3D object detection models, trained only on rectilinear images, to
detect 3D objects in images from fisheye cameras, without using any fisheye
training data. We outperform the only existing method for monocular 3D object
detection in panoramas on a benchmark of synthetic data, despite the fact that
the existing method trains on the target non-rectilinear projection whereas we
train only on rectilinear images. We also experiment with an internal dataset
of real fisheye images.
