Accurate and reliable 3D detection is vital for many applications including autonomous driving vehicles and service robots. In this paper, we present a flexible and high-performance 3D detection framework, named MPPNet, for 3D temporal object detection with point cloud sequences. We propose a novel three-hierarchy framework with proxy points for multi-frame feature encoding and interactions to achieve better detection. The three hierarchies conduct per-frame feature encoding, short-clip feature fusion, and whole-sequence feature aggregation, respectively. To enable processing long-sequence point clouds with reasonable computational resources, intra-group feature mixing and inter-group feature attention are proposed to form the second and third feature encoding hierarchies, which are recurrently applied for aggregating multi-frame trajectory features. The proxy points not only act as consistent object representations for each frame, but also serve as the courier to facilitate feature interaction between frames. The experiments on large Waymo Open dataset show that our approach outperforms state-of-the-art methods with large margins when applied to both short (e.g., 4-frame) and long (e.g., 16-frame) point cloud sequences. Code is available at https://github.com/open-mmlab/OpenPCDet.
翻译:精确可靠的三维探测对于许多应用都至关重要, 包括自主驾驶车辆和服务机器人。 在本文中, 我们提出了一个灵活和高性能的三维探测框架, 名为 MPPNet, 用于使用点云序列的三维时间天体探测; 我们提出一个新的三层结构框架, 配有多框架特征编码和互动的代用点, 以更好地检测。 三个等级组分别进行每个框架特征编码、 短曲点特征聚合和整个序列特征聚合。 为了能够用合理的计算资源处理长重点云, 提议对第二和第三个特征编码等级进行内部特征混合和群体间特征关注, 以形成第二和第三个特征编码, 用于集成多框架轨迹特征。 代用点不仅作为每个框架的一致对象表达方式, 而且还充当促进各框架之间特征互动的送信者。 大Waymo Open数据集的实验显示, 当我们的方法在短( e.g, 4- framine) 和长线/ AS- ASOMB 序列(e. musb. dealb. dealb.