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Promising complementarity exists between the texture features of color imagesand the geometric information of LiDAR point clouds. However, there stillpresent many challenges for efficient and robust feature fusion in the field of3D object detection. In this paper, first, unstructured 3D point clouds arefilled in the 2D plane and 3D point cloud features are extracted faster usingprojection-aware convolution layers. Further, the corresponding indexes betweendifferent sensor signals are established in advance in the data preprocessing,which enables faster cross-modal feature fusion. To address LiDAR points andimage pixels misalignment problems, two new plug-and-play fusion modules,LiCamFuse and BiLiCamFuse, are proposed. In LiCamFuse, soft query weights withperceiving the Euclidean distance of bimodal features are proposed. InBiLiCamFuse, the fusion module with dual attention is proposed to deeplycorrelate the geometric and textural features of the scene. The quantitativeresults on the KITTI dataset demonstrate that the proposed method achievesbetter feature-level fusion. In addition, the proposed network shows a shorterrunning time compared to existing methods.

FFPA-Net: Efficient Feature Fusion with Projection Awareness for 3D Object Detection