  We present an automatic annotation pipeline to recover 9D cuboids and 3D
shapes from pre-trained off-the-shelf 2D detectors and sparse LIDAR data. Our
autolabeling method solves an ill-posed inverse problem by considering learned
shape priors and optimizing geometric and physical parameters. To address this
challenging problem, we apply a novel differentiable shape renderer to signed
distance fields (SDF), leveraged together with normalized object coordinate
spaces (NOCS). Initially trained on synthetic data to predict shape and
coordinates, our method uses these predictions for projective and geometric
alignment over real samples. Moreover, we also propose a curriculum learning
strategy, iteratively retraining on samples of increasing difficulty in
subsequent self-improving annotation rounds. Our experiments on the KITTI3D
dataset show that we can recover a substantial amount of accurate cuboids, and
that these autolabels can be used to train 3D vehicle detectors with
state-of-the-art results.
