  Geometry Projection is a powerful depth estimation method in monocular 3D
object detection. It estimates depth dependent on heights, which introduces
mathematical priors into the deep model. But projection process also introduces
the error amplification problem, in which the error of the estimated height
will be amplified and reflected greatly at the output depth. This property
leads to uncontrollable depth inferences and also damages the training
efficiency. In this paper, we propose a Geometry Uncertainty Projection Network
(GUP Net) to tackle the error amplification problem at both inference and
training stages. Specifically, a GUP module is proposed to obtains the
geometry-guided uncertainty of the inferred depth, which not only provides high
reliable confidence for each depth but also benefits depth learning.
Furthermore, at the training stage, we propose a Hierarchical Task Learning
strategy to reduce the instability caused by error amplification. This learning
algorithm monitors the learning situation of each task by a proposed indicator
and adaptively assigns the proper loss weights for different tasks according to
their pre-tasks situation. Based on that, each task starts learning only when
its pre-tasks are learned well, which can significantly improve the stability
and efficiency of the training process. Extensive experiments demonstrate the
effectiveness of the proposed method. The overall model can infer more reliable
object depth than existing methods and outperforms the state-of-the-art
image-based monocular 3D detectors by 3.74% and 4.7% AP40 of the car and
pedestrian categories on the KITTI benchmark.
