The ability to reliably perceive the environmental states, particularly the
existence of objects and their motion behavior, is crucial for autonomous
driving. In this work, we propose an efficient deep model, called MotionNet, to
jointly perform perception and motion prediction from 3D point clouds.
MotionNet takes a sequence of LiDAR sweeps as input and outputs a bird's eye
view (BEV) map, which encodes the object category and motion information in
each grid cell. The backbone of MotionNet is a novel spatio-temporal pyramid
network, which extracts deep spatial and temporal features in a hierarchical
fashion. To enforce the smoothness of predictions over both space and time, the
training of MotionNet is further regularized with novel spatial and temporal
consistency losses. Extensive experiments show that the proposed method overall
outperforms the state-of-the-arts, including the latest scene-flow- and
3D-object-detection-based methods. This indicates the potential value of the
proposed method serving as a backup to the bounding-box-based system, and
providing complementary information to the motion planner in autonomous
driving. Code is available at this https URL

本文提出了一种高效的深度学习模型 MotionNet，通过 LiDAR 扫描的数据实现自动驾驶中的感知和运动预测，该模型基于一种新颖的时空金字塔网络，可以提取深层次的时空特征，并且通过空间和时间一致性约束进一步规范了模型的训练。实验证明，该方法的性能明显优于现有的基于场景流和 3D 目标检测的方法，有望在自动驾驶中提供备用方案和对运动规划员提供补充信息。