  3D object detection from monocular images is an ill-posed problem due to the
projective entanglement of depth and scale. To overcome this ambiguity, we
present a novel self-supervised method for textured 3D shape reconstruction and
pose estimation of rigid objects with the help of strong shape priors and 2D
instance masks. Our method predicts the 3D location and meshes of each object
in an image using differentiable rendering and a self-supervised objective
derived from a pretrained monocular depth estimation network. We use the KITTI
3D object detection dataset to evaluate the accuracy of the method. Experiments
demonstrate that we can effectively use noisy monocular depth and
differentiable rendering as an alternative to expensive 3D ground-truth labels
or LiDAR information.
