  The estimation of the orientation of an observed vehicle relative to an
Autonomous Vehicle (AV) from monocular camera data is an important building
block in estimating its 6 DoF pose. Current Deep Learning based solutions for
placing a 3D bounding box around this observed vehicle are data hungry and do
not generalize well. In this paper, we demonstrate the use of monocular visual
odometry for the self-supervised fine-tuning of a model for orientation
estimation pre-trained on a reference domain. Specifically, while transitioning
from a virtual dataset (vKITTI) to nuScenes, we recover up to 70% of the
performance of a fully supervised method. We subsequently demonstrate an
optimization-based monocular 3D bounding box detector built on top of the
self-supervised vehicle orientation estimator without the requirement of
expensive labeled data. This allows 3D vehicle detection algorithms to be
self-trained from large amounts of monocular camera data from existing
commercial vehicle fleets.
