The precise localization of 3D objects from a single image without depth
information is a highly challenging problem. Most existing methods adopt the
same approach for all objects regardless of their diverse distributions,
leading to limited performance for truncated objects. In this paper, we propose
a flexible framework for monocular 3D object detection which explicitly
decouples the truncated objects and adaptively combines multiple approaches for
object depth estimation. Specifically, we decouple the edge of the feature map
for predicting long-tail truncated objects so that the optimization of normal
objects is not influenced. Furthermore, we formulate the object depth
estimation as an uncertainty-guided ensemble of directly regressed object depth
and solved depths from different groups of keypoints. Experiments demonstrate
that our method outperforms the state-of-the-art method by relatively 27\% for
the moderate level and 30\% for the hard level in the test set of KITTI
benchmark while maintaining real-time efficiency. Code will be available at
https://github.com/zhangyp15/MonoFlex.

提出了一种灵活的单目 3D 物体检测框架，它显式地分离了长尾切断物体，并自适应地组合多种方法来估计物体深度。

物体不同：灵活的单目三维物体检测

Objects are Different: Flexible Monocular 3D Object Detection

Modern 3D object detectors have immensely benefited from the end-to-end
learning idea. However, most of them use a post-processing algorithm called
Non-Maximal Suppression (NMS) only during inference. While there were attempts
to include NMS in the training pipeline for tasks such as 2D object detection,
they have been less widely adopted due to a non-mathematical expression of the
NMS. In this paper, we present and integrate GrooMeD-NMS -- a novel Grouped
Mathematically Differentiable NMS for monocular 3D object detection, such that
the network is trained end-to-end with a loss on the boxes after NMS. We first
formulate NMS as a matrix operation and then group and mask the boxes in an
unsupervised manner to obtain a simple closed-form expression of the NMS.
GrooMeD-NMS addresses the mismatch between training and inference pipelines
and, therefore, forces the network to select the best 3D box in a
differentiable manner. As a result, GrooMeD-NMS achieves state-of-the-art
monocular 3D object detection results on the KITTI benchmark dataset performing
comparably to monocular video-based methods. Code and models at
this https URL

本文提出了一种基于分组数学可微分 NMS 的对象检测方法，通过在训练过程中损失 NMS 之后的框来实现端到端的训练，并在 KITTI 评估数据集上实现了最先进的单眼 3D 对象检测结果。