SSD-MonoDETR: Supervised Scale-aware Deformable Transformer for Monocular 3D Object Detection
This work addresses a bottleneck in monocular 3D object detection for autonomous driving by introducing a supervised attention method to enhance query feature generation, representing an incremental improvement over existing transformer-based approaches.
The paper tackles the problem of low-quality query features in transformer-based monocular 3D object detection by proposing a Supervised Scale-aware Deformable Attention (SSDA) mechanism, which improves detection accuracy, achieving state-of-the-art performance on KITTI and Waymo Open datasets, especially on moderate and hard objects.
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.