论文标题
来自LiDAR数据的车辆的关节姿势和形状估计
Joint Pose and Shape Estimation of Vehicles from LiDAR Data
论文作者
论文摘要
我们解决了估计LiDar Scans车辆的姿势和形状的问题，LiDAR扫描是自动驾驶汽车社区所面临的常见问题。尽管两者之间存在固有的联系，但最近的工作倾向于分别解决姿势和形状估计。我们研究了一种共同估计形状和姿势的方法，其中学习了单个编码，可以从中可以以有效但有效的方式解码形状和姿势。我们还引入了一种新颖的关节姿势和形状损失，并表明这种关节训练方法比独立训练的姿势和形状估计器产生更好的结果。我们在合成数据和现实世界数据上评估了我们的方法，并在最先进的基准方面显示出卓越的性能。
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.
