S$^3$-MonoDETR: Supervised Shape&Scale-perceptive Deformable Transformer
  for Monocular 3D Object Detection
Xuan He Kailun Yang Junwei Zheng Jin Yuan Luis M. Bergasa Hui Zhang Zhiyong Li
Recently, transformer-based methods have shown exceptional performance in
monocular 3D object detection, which can predict 3D attributes from a single 2D
image. These methods typically use visual and depth representations to generate
query points on objects, whose quality plays a decisive role in the detection
accuracy. However, current unsupervised attention mechanisms without any
geometry appearance awareness in transformers are susceptible to producing
noisy features for query points, which severely limits the network performance
and also makes the model have a poor ability to detect multi-category objects
in a single training process. To tackle this problem, this paper proposes a
novel "Supervised Shape&Scale-perceptive Deformable Attention" (S$^3$-DA)
module for monocular 3D object detection. Concretely, S$^3$-DA utilizes visual
and depth features to generate diverse local features with various shapes and
scales and predict the corresponding matching distribution simultaneously to
impose valuable shape&scale perception for each query. Benefiting from this,
S$^3$-DA effectively estimates receptive fields for query points belonging to
any category, enabling them to generate robust query features. Besides, we
propose a Multi-classification-based Shape$\&$Scale Matching (MSM) loss to
supervise the above process. Extensive experiments on KITTI and Waymo Open
datasets demonstrate that S$^3$-DA significantly improves the detection
accuracy, yielding state-of-the-art performance of single-category and
multi-category 3D object detection in a single training process compared to the
existing approaches. The source code will be made publicly available at
https://github.com/mikasa3lili/S3-MonoDETR.