DynStatF: An Efficient Feature Fusion Strategy for LiDAR 3D Object
  Detection
Yao Rong Xiangyu Wei Tianwei Lin Yueyu Wang Enkelejda Kasneci
Augmenting LiDAR input with multiple previous frames provides richer semantic
information and thus boosts performance in 3D object detection, However,
crowded point clouds in multi-frames can hurt the precise position information
due to the motion blur and inaccurate point projection. In this work, we
propose a novel feature fusion strategy, DynStaF (Dynamic-Static Fusion), which
enhances the rich semantic information provided by the multi-frame (dynamic
branch) with the accurate location information from the current single-frame
(static branch). To effectively extract and aggregate complimentary features,
DynStaF contains two modules, Neighborhood Cross Attention (NCA) and
Dynamic-Static Interaction (DSI), operating through a dual pathway
architecture. NCA takes the features in the static branch as queries and the
features in the dynamic branch as keys (values). When computing the attention,
we address the sparsity of point clouds and take only neighborhood positions
into consideration. NCA fuses two features at different feature map scales,
followed by DSI providing the comprehensive interaction. To analyze our
proposed strategy DynStaF, we conduct extensive experiments on the nuScenes
dataset. On the test set, DynStaF increases the performance of PointPillars in
NDS by a large margin from 57.7% to 61.6%. When combined with CenterPoint, our
framework achieves 61.0% mAP and 67.7% NDS, leading to state-of-the-art
performance without bells and whistles.