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。