  Most scanning LiDAR sensors generate a sequence of point clouds in real-time.
While conventional 3D object detectors use a set of unordered LiDAR points
acquired over a fixed time interval, recent studies have revealed that
substantial performance improvement can be achieved by exploiting the
spatio-temporal context present in a sequence of LiDAR point sets. In this
paper, we propose a novel 3D object detection architecture, which can encode
LiDAR point cloud sequences acquired by multiple successive scans. The encoding
process of the point cloud sequence is performed on two different time scales.
We first design a short-term motion-aware voxel encoding that captures the
short-term temporal changes of point clouds driven by the motion of objects in
each voxel. We also propose long-term motion-guided bird's eye view (BEV)
feature enhancement that adaptively aligns and aggregates the BEV feature maps
obtained by the short-term voxel encoding by utilizing the dynamic motion
context inferred from the sequence of the feature maps. The experiments
conducted on the public nuScenes benchmark demonstrate that the proposed 3D
object detector offers significant improvements in performance compared to the
baseline methods and that it sets a state-of-the-art performance for certain 3D
object detection categories. Code is available at
https://github.com/HYjhkoh/MGTANet.git
