  Low-cost monocular 3D object detection plays a fundamental role in autonomous
driving, whereas its accuracy is still far from satisfactory. In this paper, we
dig into the 3D object detection task and reformulate it as the sub-tasks of
object localization and appearance perception, which benefits to a deep
excavation of reciprocal information underlying the entire task. We introduce a
Dynamic Feature Reflecting Network, named DFR-Net, which contains two novel
standalone modules: (i) the Appearance-Localization Feature Reflecting module
(ALFR) that first separates taskspecific features and then self-mutually
reflects the reciprocal features; (ii) the Dynamic Intra-Trading module (DIT)
that adaptively realigns the training processes of various sub-tasks via a
self-learning manner. Extensive experiments on the challenging KITTI dataset
demonstrate the effectiveness and generalization of DFR-Net. We rank 1st among
all the monocular 3D object detectors in the KITTI test set (till March 16th,
2021). The proposed method is also easy to be plug-and-play in many
cutting-edge 3D detection frameworks at negligible cost to boost performance.
The code will be made publicly available.
