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We address the problem of estimating the pose and shape of vehicles fromLiDAR scans, a common problem faced by the autonomous vehicle community. Recentwork has tended to address pose and shape estimation separately in isolation,despite the inherent connection between the two. We investigate a method ofjointly estimating shape and pose where a single encoding is learned from whichshape and pose may be decoded in an efficient yet effective manner. Weadditionally introduce a novel joint pose and shape loss, and show that thisjoint training method produces better results than independently-trained poseand shape estimators. We evaluate our method on both synthetic data andreal-world data, and show superior performance against a state-of-the-artbaseline.

Joint Pose and Shape Estimation of Vehicles from LiDAR Data