  Autonomous driving requires 3D perception of vehicles and other objects in
the in environment. Much of the current methods support 2D vehicle detection.
This paper proposes a flexible pipeline to adopt any 2D detection network and
fuse it with a 3D point cloud to generate 3D information with minimum changes
of the 2D detection networks. To identify the 3D box, an effective model
fitting algorithm is developed based on generalised car models and score maps.
A two-stage convolutional neural network (CNN) is proposed to refine the
detected 3D box. This pipeline is tested on the KITTI dataset using two
different 2D detection networks. The 3D detection results based on these two
networks are similar, demonstrating the flexibility of the proposed pipeline.
The results rank second among the 3D detection algorithms, indicating its
competencies in 3D detection.
