  Aiming at highly accurate object detection for connected and automated
vehicles (CAVs), this paper presents a Deep Neural Network based 3D object
detection model that leverages a three-stage feature extractor by developing a
novel LIDAR-Camera fusion scheme. The proposed feature extractor extracts
high-level features from two input sensory modalities and recovers the
important features discarded during the convolutional process. The novel fusion
scheme effectively fuses features across sensory modalities and convolutional
layers to find the best representative global features. The fused features are
shared by a two-stage network: the region proposal network (RPN) and the
detection head (DH). The RPN generates high-recall proposals, and the DH
produces final detection results. The experimental results show the proposed
model outperforms more recent research on the KITTI 2D and 3D detection
benchmark, particularly for distant and highly occluded instances.
