  Point clouds and images could provide complementary information when
representing 3D objects. Fusing the two kinds of data usually helps to improve
the detection results. However, it is challenging to fuse the two data
modalities, due to their different characteristics and the interference from
the non-interest areas. To solve this problem, we propose a Multi-Branch Deep
Fusion Network (MBDF-Net) for 3D object detection. The proposed detector has
two stages. In the first stage, our multi-branch feature extraction network
utilizes Adaptive Attention Fusion (AAF) modules to produce cross-modal fusion
features from single-modal semantic features. In the second stage, we use a
region of interest (RoI) -pooled fusion module to generate enhanced local
features for refinement. A novel attention-based hybrid sampling strategy is
also proposed for selecting key points in the downsampling process. We evaluate
our approach on two widely used benchmark datasets including KITTI and
SUN-RGBD. The experimental results demonstrate the advantages of our method
over state-of-the-art approaches.
