  3D object detection has become an emerging task in autonomous driving
scenarios. Previous works process 3D point clouds using either projection-based
or voxel-based models. However, both approaches contain some drawbacks. The
voxel-based methods lack semantic information, while the projection-based
methods suffer from numerous spatial information loss when projected to
different views. In this paper, we propose the Stereo RGB and Deeper LIDAR
(SRDL) framework which can utilize semantic and spatial information
simultaneously such that the performance of network for 3D object detection can
be improved naturally. Specifically, the network generates candidate boxes from
stereo pairs and combines different region-wise features using a deep fusion
scheme. The stereo strategy offers more information for prediction compared
with prior works. Then, several local and global feature extractors are stacked
in the segmentation module to capture richer deep semantic geometric features
from point clouds. After aligning the interior points with fused features, the
proposed network refines the prediction in a more accurate manner and encodes
the whole box in a novel compact method. The decent experimental results on the
challenging KITTI detection benchmark demonstrate the effectiveness of
utilizing both stereo images and point clouds for 3D object detection.
