  In this paper, we propose to learn a deep fitting degree scoring network for
monocular 3D object detection, which aims to score fitting degree between
proposals and object conclusively. Different from most existing monocular
frameworks which use tight constraint to get 3D location, our approach achieves
high-precision localization through measuring the visual fitting degree between
the projected 3D proposals and the object. We first regress the dimension and
orientation of the object using an anchor-based method so that a suitable 3D
proposal can be constructed. We propose FQNet, which can infer the 3D IoU
between the 3D proposals and the object solely based on 2D cues. Therefore,
during the detection process, we sample a large number of candidates in the 3D
space and project these 3D bounding boxes on 2D image individually. The best
candidate can be picked out by simply exploring the spatial overlap between
proposals and the object, in the form of the output 3D IoU score of FQNet.
Experiments on the KITTI dataset demonstrate the effectiveness of our
framework.
