Monocular 3D object detection is a low-cost but challenging task, as it requires generating accurate 3D localization solely from a single image input. Recent developed depth-assisted methods show promising results by using explicit depth maps as intermediate features, which are either precomputed by monocular depth estimation networks or jointly evaluated with 3D object detection. However, inevitable errors from estimated depth priors may lead to misaligned semantic information and 3D localization, hence resulting in feature smearing and suboptimal predictions. To mitigate this issue, we propose ADD, an Attention-based Depth knowledge Distillation framework with 3D-aware positional encoding. Unlike previous knowledge distillation frameworks that adopt stereo- or LiDAR-based teachers, we build up our teacher with identical architecture as the student but with extra ground-truth depth as input. Credit to our teacher design, our framework is seamless, domain-gap free, easily implementable, and is compatible with object-wise ground-truth depth. Specifically, we leverage intermediate features and responses for knowledge distillation. Considering long-range 3D dependencies, we propose \emph{3D-aware self-attention} and \emph{target-aware cross-attention} modules for student adaptation. Extensive experiments are performed to verify the effectiveness of our framework on the challenging KITTI 3D object detection benchmark. We implement our framework on three representative monocular detectors, and we achieve state-of-the-art performance with no additional inference computational cost relative to baseline models. Our code is available at https://github.com/rockywind/ADD.

翻译：单目三维目标检测是一项成本较低但极具挑战性的任务，因为其需要仅从单一图像输入生成精确的三维定位。近年来发展起来的深度辅助方法通过使用显式深度图作为中间特征展现出良好前景，这些深度图要么由单目深度估计网络预先计算，要么与三维目标检测联合评估。然而，估计深度先验中不可避免的误差可能导致语义信息与三维定位不对齐，从而造成特征模糊和次优预测。为缓解这一问题，我们提出ADD——一种基于注意力的深度知识蒸馏框架，并引入三维感知位置编码。与以往采用立体或激光雷达教师的蒸馏框架不同，我们构建的教师模型与学生模型结构相同，但额外以真实深度作为输入。得益于我们的教师设计，该框架无缝衔接、无领域差距、易于实现，并且兼容逐目标真实深度。具体而言，我们利用中间特征和响应进行知识蒸馏。考虑到三维长程依赖，我们提出了三维感知自注意力和目标感知交叉注意力模块用于学生适配。在具有挑战性的KITTI三维目标检测基准上进行了大量实验，验证了我们框架的有效性。我们在三种代表性单目检测器上实现该框架，在不增加推理计算开销的情况下，相较于基线模型达到了最先进性能。我们的代码可在https://github.com/rockywind/ADD获取。