  Monocular 3D object detection has recently shown promising results, however
there remain challenging problems. One of those is the lack of invariance to
different camera intrinsic parameters, which can be observed across different
3D object datasets. Little effort has been made to exploit the combination of
heterogeneous 3D object datasets. In contrast to general intuition, we show
that more data does not automatically guarantee a better performance, but
rather, methods need to have a degree of 'camera independence' in order to
benefit from large and heterogeneous training data. In this paper we propose a
category-level pose estimation method based on instance segmentation, using
camera independent geometric reasoning to cope with the varying camera
viewpoints and intrinsics of different datasets. Every pixel of an instance
predicts the object dimensions, the 3D object reference points projected in 2D
image space and, optionally, the local viewing angle. Camera intrinsics are
only used outside of the learned network to lift the predicted 2D reference
points to 3D. We surpass camera independent methods on the challenging KITTI3D
benchmark and show the key benefits compared to camera dependent methods.
