  Promising complementarity exists between the texture features of color images
and the geometric information of LiDAR point clouds. However, there still
present many challenges for efficient and robust feature fusion in the field of
3D object detection. In this paper, first, unstructured 3D point clouds are
filled in the 2D plane and 3D point cloud features are extracted faster using
projection-aware convolution layers. Further, the corresponding indexes between
different sensor signals are established in advance in the data preprocessing,
which enables faster cross-modal feature fusion. To address LiDAR points and
image pixels misalignment problems, two new plug-and-play fusion modules,
LiCamFuse and BiLiCamFuse, are proposed. In LiCamFuse, soft query weights with
perceiving the Euclidean distance of bimodal features are proposed. In
BiLiCamFuse, the fusion module with dual attention is proposed to deeply
correlate the geometric and textural features of the scene. The quantitative
results on the KITTI dataset demonstrate that the proposed method achieves
better feature-level fusion. In addition, the proposed network shows a shorter
running time compared to existing methods.
