  Estimating 3D bounding boxes from monocular images is an essential component
in autonomous driving, while accurate 3D object detection from this kind of
data is very challenging. In this work, by intensive diagnosis experiments, we
quantify the impact introduced by each sub-task and found the `localization
error' is the vital factor in restricting monocular 3D detection. Besides, we
also investigate the underlying reasons behind localization errors, analyze the
issues they might bring, and propose three strategies. First, we revisit the
misalignment between the center of the 2D bounding box and the projected center
of the 3D object, which is a vital factor leading to low localization accuracy.
Second, we observe that accurately localizing distant objects with existing
technologies is almost impossible, while those samples will mislead the learned
network. To this end, we propose to remove such samples from the training set
for improving the overall performance of the detector. Lastly, we also propose
a novel 3D IoU oriented loss for the size estimation of the object, which is
not affected by `localization error'. We conduct extensive experiments on the
KITTI dataset, where the proposed method achieves real-time detection and
outperforms previous methods by a large margin. The code will be made available
at: https://github.com/xinzhuma/monodle.
