Deep Fitting Degree Scoring Network for Monocular 3D Object Detection

Understand

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.