论文标题
任何运动探测器：从激光雷达点云中学习类无形的场景动态
Any Motion Detector: Learning Class-agnostic Scene Dynamics from a Sequence of LiDAR Point Clouds
论文作者
论文摘要
对象检测和运动参数估计是在复杂的城市环境中自动驾驶车辆安全导航的关键任务。在这项工作中，我们提出了一种基于3D点云序列的运动检测和运动参数估算的时间上下文聚集的新型实时方法。我们引入了一个自我运动补偿层，以实现实时推断，其性能与原始点云序列的天真渗透压变化相当。拟议的建筑不仅能够估算诸如车辆或行人之类的普通公路参与者的运动，而且还可以将培训数据中不存在的其他物体类别推广。我们还对不同的时间上下文聚集策略（例如复发细胞和3D卷积）进行了深入的分析。最后，我们提供了最新模型与Kitti场景流数据集中的现有解决方案的比较结果。
Object detection and motion parameters estimation are crucial tasks for self-driving vehicle safe navigation in a complex urban environment. In this work we propose a novel real-time approach of temporal context aggregation for motion detection and motion parameters estimation based on 3D point cloud sequence. We introduce an ego-motion compensation layer to achieve real-time inference with performance comparable to a naive odometric transform of the original point cloud sequence. Not only is the proposed architecture capable of estimating the motion of common road participants like vehicles or pedestrians but also generalizes to other object categories which are not present in training data. We also conduct an in-deep analysis of different temporal context aggregation strategies such as recurrent cells and 3D convolutions. Finally, we provide comparison results of our state-of-the-art model with existing solutions on KITTI Scene Flow dataset.
