  Transformer-based methods have demonstrated superior performance for
monocular 3D object detection recently, which aims at predicting 3D attributes
from a single 2D image. Most existing transformer-based methods leverage both
visual and depth representations to explore valuable query points on objects,
and the quality of the learned query points has a great impact on detection
accuracy. Unfortunately, existing unsupervised attention mechanisms in
transformers are prone to generate low-quality query features due to inaccurate
receptive fields, especially on hard objects. To tackle this problem, this
paper proposes a novel "Supervised Scale-aware Deformable Attention" (SSDA) for
monocular 3D object detection. Specifically, SSDA presets several masks with
different scales and utilizes depth and visual features to adaptively learn a
scale-aware filter for object query augmentation. Imposing the scale awareness,
SSDA could well predict the accurate receptive field of an object query to
support robust query feature generation. Aside from this, SSDA is assigned with
a Weighted Scale Matching (WSM) loss to supervise scale prediction, which
presents more confident results as compared to the unsupervised attention
mechanisms. Extensive experiments on the KITTI and Waymo Open datasets
demonstrate that SSDA significantly improves the detection accuracy, especially
on moderate and hard objects, yielding state-of-the-art performance as compared
to the existing approaches. Our code will be made publicly available at
https://github.com/mikasa3lili/SSD-MonoDETR.
