  3D object detection is an important module in autonomous driving and
robotics. However, many existing methods focus on using single frames to
perform 3D detection, and do not fully utilize information from multiple
frames. In this paper, we present 3D-MAN: a 3D multi-frame attention network
that effectively aggregates features from multiple perspectives and achieves
state-of-the-art performance on Waymo Open Dataset. 3D-MAN first uses a novel
fast single-frame detector to produce box proposals. The box proposals and
their corresponding feature maps are then stored in a memory bank. We design a
multi-view alignment and aggregation module, using attention networks, to
extract and aggregate the temporal features stored in the memory bank. This
effectively combines the features coming from different perspectives of the
scene. We demonstrate the effectiveness of our approach on the large-scale
complex Waymo Open Dataset, achieving state-of-the-art results compared to
published single-frame and multi-frame methods.
