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.

翻译：近期，基于Transformer的方法在单目三维目标检测中展现出卓越性能，能够从单张二维图像预测三维属性。这类方法通常利用视觉与深度表征在目标上生成查询点，其质量对检测精度具有决定性影响。然而，当前Transformer中缺乏几何外观感知的无监督注意力机制，易导致查询点产生噪声特征，严重限制网络性能，且使模型在单次训练过程中难以有效检测多类别目标。针对该问题，本文提出新型"监督式形状与尺度感知变形注意力"（S$^3$-DA）模块用于单目3D目标检测。具体而言，S$^3$-DA利用视觉与深度特征生成具有不同形状和尺度的多样化局部特征，同时预测对应的匹配分布，为每个查询赋予有价值的形状与尺度感知能力。得益于此，S$^3$-DA能有效估计任意类别目标查询点的感受野，使其生成鲁棒的查询特征。此外，我们提出基于多分类的形状与尺度匹配（MSM）损失函数来监督上述过程。在KITTI和Waymo Open数据集上的大量实验表明，与现有方法相比，S$^3$-DA显著提升了检测精度，在单次训练过程中实现了单类别与多类别三维目标检测的最新性能。源代码将公开发布于https://github.com/mikasa3lili/S3-MonoDETR。