  This paper focuses on the construction of stronger local features and the
effective fusion of image and LiDAR data. We adopt different modalities of
LiDAR data to generate richer features and present an adaptive and
azimuth-aware network to aggregate local features from image, bird's eye view
maps and point cloud. Our network mainly consists of three subnetworks: ground
plane estimation network, region proposal network and adaptive fusion network.
The ground plane estimation network extracts features of point cloud and
predicts the parameters of a plane which are used for generating abundant 3D
anchors. The region proposal network generates features of image and bird's eye
view maps to output region proposals. To integrate heterogeneous image and
point cloud features, the adaptive fusion network explicitly adjusts the
intensity of multiple local features and achieves the orientation consistency
between image and LiDAR data by introduce an azimuth-aware fusion module.
Experiments are conducted on KITTI dataset and the results validate the
advantages of our aggregation of multimodal local features and the adaptive
fusion network.
