  The paper proposes a light-weighted stereo frustums matching module for 3D
objection detection. The proposed framework takes advantage of a
high-performance 2D detector and a point cloud segmentation network to regress
3D bounding boxes for autonomous driving vehicles. Instead of performing
traditional stereo matching to compute disparities, the module directly takes
the 2D proposals from both the left and the right views as input. Based on the
epipolar constraints recovered from the well-calibrated stereo cameras, we
propose four matching algorithms to search for the best match for each proposal
between the stereo image pairs. Each matching pair proposes a segmentation of
the scene which is then fed into a 3D bounding box regression network. Results
of extensive experiments on KITTI dataset demonstrate that the proposed Siamese
pipeline outperforms the state-of-the-art stereo-based 3D bounding box
regression methods.
