In this work, we propose \textit{MVFuseNet}, a novel end-to-end method for
joint object detection and motion forecasting from a temporal sequence of LiDAR
data. Most existing methods operate in a single view by projecting data in
either range view (RV) or bird's eye view (BEV). In contrast, we propose a
method that effectively utilizes both RV and BEV for spatio-temporal feature
learning as part of a temporal fusion network as well as for multi-scale
feature learning in the backbone network. Further, we propose a novel
sequential fusion approach that effectively utilizes multiple views in the
temporal fusion network. We show the benefits of our multi-view approach for
the tasks of detection and motion forecasting on two large-scale self-driving
data sets, achieving state-of-the-art results. Furthermore, we show that
MVFusenet scales well to large operating ranges while maintaining real-time
performance.

提出了一种名为 MVFuseNet 的方法，在利用脉冲激光雷达（LiDAR）数据时，通过多视图联合学习以及多尺度特征提取的方式，实现了目标检测和运动预测，取得了最新的技术成果，并且验证了该方法在大范围的自动驾驶数据集上可以很好地拓展并保持实时性能。