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Monocular 3D object detection has recently shown promising results, howeverthere remain challenging problems. One of those is the lack of invariance todifferent camera intrinsic parameters, which can be observed across different3D object datasets. Little effort has been made to exploit the combination ofheterogeneous 3D object datasets. In contrast to general intuition, we showthat more data does not automatically guarantee a better performance, butrather, methods need to have a degree of 'camera independence' in order tobenefit from large and heterogeneous training data. In this paper we propose acategory-level pose estimation method based on instance segmentation, usingcamera independent geometric reasoning to cope with the varying cameraviewpoints and intrinsics of different datasets. Every pixel of an instancepredicts the object dimensions, the 3D object reference points projected in 2Dimage space and, optionally, the local viewing angle. Camera intrinsics areonly used outside of the learned network to lift the predicted 2D referencepoints to 3D. We surpass camera independent methods on the challenging KITTI3Dbenchmark and show the key benefits compared to camera dependent methods.

MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation