Joint Pose and Shape Estimation of Vehicles from LiDAR Data

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.