  LiDAR sensors are widely used for 3D object detection in various mobile
robotics applications. LiDAR sensors continuously generate point cloud data in
real-time. Conventional 3D object detectors detect objects using a set of
points acquired over a fixed duration. However, recent studies have shown that
the performance of object detection can be further enhanced by utilizing
spatio-temporal information obtained from point cloud sequences. In this paper,
we propose a new 3D object detector, named D-Align, which can effectively
produce strong bird's-eye-view (BEV) features by aligning and aggregating the
features obtained from a sequence of point sets. The proposed method includes a
novel dual-query co-attention network that uses two types of queries, including
target query set (T-QS) and support query set (S-QS), to update the features of
target and support frames, respectively. D-Align aligns S-QS to T-QS based on
the temporal context features extracted from the adjacent feature maps and then
aggregates S-QS with T-QS using a gated attention mechanism. The dual queries
are updated through multiple attention layers to progressively enhance the
target frame features used to produce the detection results. Our experiments on
the nuScenes dataset show that the proposed D-Align method greatly improved the
performance of a single frame-based baseline method and significantly
outperformed the latest 3D object detectors.
