In this paper we propose an approach for monocular 3D object detection from a
single RGB image, which leverages a novel disentangling transformation for 2D
and 3D detection losses and a novel, self-supervised confidence score for 3D
bounding boxes. Our proposed loss disentanglement has the twofold advantage of
simplifying the training dynamics in the presence of losses with complex
interactions of parameters, and sidestepping the issue of balancing independent
regression terms. Our solution overcomes these issues by isolating the
contribution made by groups of parameters to a given loss, without changing its
nature. We further apply loss disentanglement to another novel, signed
Intersection-over-Union criterion-driven loss for improving 2D detection
results. Besides our methodological innovations, we critically review the AP
metric used in KITTI3D, which emerged as the most important dataset for
comparing 3D detection results. We identify and resolve a flaw in the 11-point
interpolated AP metric, affecting all previously published detection results
and particularly biases the results of monocular 3D detection. We provide
extensive experimental evaluations and ablation studies on the KITTI3D and
nuScenes datasets, setting new state-of-the-art results on object category car
by large margins.

该研究提出了一种通过单个 RGB 图像实现单目 3D 物体检测的方法，采用新颖的去耦合转换来实现 2D 和 3D 检测损失，以及一种新颖的自监督置信度分数来实现 3D 边界框。该方法通过隔离参数组对给定损失的贡献来解决了复杂互动参数存在的问题。同时，作者还通过改善 2D 检测结果的有符号交并比驱动损失来应用了损失去耦合。通过基于 KITTI3D 和 nuScenes 数据集的广泛实验评估和消融研究，该方法在车类别目标检测方面实现了新的最优成果。

单目三维物体检测分离

Disentangling Monocular 3D Object Detection

We present a deep learning method for end-to-end monocular 3D object
detection and metric shape retrieval. We propose a novel loss formulation by
lifting 2D detection, orientation, and scale estimation into 3D space. Instead
of optimizing these quantities separately, the 3D instantiation allows to
properly measure the metric misalignment of boxes. We experimentally show that
our 10D lifting of sparse 2D Regions of Interests (RoIs) achieves great results
both for 6D pose and recovery of the textured metric geometry of instances.
This further enables 3D synthetic data augmentation via inpainting recovered
meshes directly onto the 2D scenes. We evaluate on KITTI3D against other strong
monocular methods and demonstrate that our approach doubles the AP on the 3D
pose metrics on the official test set, defining the new state of the art.

本文提出了一种深度学习方法，它可以通过端到端的单目 3D 物体检测和度量形状检索，通过提出新的损失公式将 2D 检测、方向和尺度估计提升到 3D 空间，并在 KITTI3D 数据集上展示了与其他强大单目方法和新的最佳结果相比，对 6D 姿态和纹理度量几何学的实例恢复表现出了优异的结果。