  We address the problem of estimating the pose and shape of vehicles from
LiDAR scans, a common problem faced by the autonomous vehicle community. Recent
work has tended to address pose and shape estimation separately in isolation,
despite the inherent connection between the two. We investigate a method of
jointly estimating shape and pose where a single encoding is learned from which
shape and pose may be decoded in an efficient yet effective manner. We
additionally introduce a novel joint pose and shape loss, and show that this
joint training method produces better results than independently-trained pose
and shape estimators. We evaluate our method on both synthetic data and
real-world data, and show superior performance against a state-of-the-art
baseline.
