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 this https URL

提出了一种名为 ADD 的基于注意力的深度知识蒸馏框架，采用 3D 感知的位置编码方式，以在 KITTI 3D 目标检测基准测试上实现单目 3D 目标检测的最先进性能。