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.

本文提出了一种新的实时方法，基于三维点云序列，采用时间上下文聚合实现动态检测和运动参数估计，可适用于自动驾驶车辆在复杂城市环境下进行安全导航，不仅可以估计车辆或行人等常见道路参与者的运动，还可推广到其他没有在训练数据中出现的物体类别，并对不同的时间上下文聚合策略进行了深入的分析，提供了我们最先进模型与 KITTI 场景流数据集上现有解决方案的比较结果。