  The training of deep-learning-based 3D object detectors requires large
datasets with 3D bounding box labels for supervision that have to be generated
by hand-labeling. We propose a network architecture and training procedure for
learning monocular 3D object detection without 3D bounding box labels. By
representing the objects as triangular meshes and employing differentiable
shape rendering, we define loss functions based on depth maps, segmentation
masks, and ego- and object-motion, which are generated by pre-trained,
off-the-shelf networks. We evaluate the proposed algorithm on the real-world
KITTI dataset and achieve promising performance in comparison to
state-of-the-art methods requiring 3D bounding box labels for training and
superior performance to conventional baseline methods.
