Monocular 3D object detection is an inherently ill-posed problem, as it is
challenging to predict accurate 3D localization from a single image. Existing
monocular 3D detection knowledge distillation methods usually project the LiDAR
onto the image plane and train the teacher network accordingly. Transferring
LiDAR-based model knowledge to RGB-based models is more complex, so a general
distillation strategy is needed. To alleviate cross-modal prob-lem, we propose
MonoSKD, a novel Knowledge Distillation framework for Monocular 3D detection
based on Spearman correlation coefficient, to learn the relative correlation
between cross-modal features. Considering the large gap between these features,
strict alignment of features may mislead the training, so we propose a looser
Spearman loss. Furthermore, by selecting appropriate distillation locations and
removing redundant modules, our scheme saves more GPU resources and trains
faster than existing methods. Extensive experiments are performed to verify the
effectiveness of our framework on the challenging KITTI 3D object detection
benchmark. Our method achieves state-of-the-art performance until submission
with no additional inference computational cost. Our codes are available at
this https URL

基于 Spearman 相关系数的单目 3D 检测的知识蒸馏框架 MonoSKD，在 KITTI 3D 目标检测基准测试上取得了最先进的性能，没有额外的推理计算成本。

MonoSKD：通过 Spearman 等级相关系数进行单目三维物体检测的通用蒸馏框架

MonoSKD: General Distillation Framework for Monocular 3D Object  Detection via Spearman Correlation Coefficient

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。

基于注意力机制的深度蒸馏技术与三维感知位置编码在单目三维物体检测中的应用

Attention-based Depth Distillation with 3D-Aware Positional Encoding for  Monocular 3D Object Detection

We propose Shift R-CNN, a hybrid model for monocular 3D object detection,
which combines deep learning with the power of geometry. We adapt a Faster
R-CNN network for regressing initial 2D and 3D object properties and combine it
with a least squares solution for the inverse 2D to 3D geometric mapping
problem, using the camera projection matrix. The closed-form solution of the
mathematical system, along with the initial output of the adapted Faster R-CNN
are then passed through a final ShiftNet network that refines the result using
our newly proposed Volume Displacement Loss. Our novel, geometrically
constrained deep learning approach to monocular 3D object detection obtains top
results on KITTI 3D Object Detection Benchmark, being the best among all
monocular methods that do not use any pre-trained network for depth estimation.

Shift R-CNN 是一种用于单目 3D 物体检测的混合模型，结合了深度学习和几何优势，利用几何约束的深度学习方法在 KITTI 3D Object Detection Benchmark 上取得了最佳结果。