Monocular 3D object detection aims to predict the object location, dimension
and orientation in 3D space alongside the object category given only a
monocular image. It poses a great challenge due to its ill-posed property which
is critically lack of depth information in the 2D image plane. While there
exist approaches leveraging off-the-shelve depth estimation or relying on LiDAR
sensors to mitigate this problem, the dependence on the additional depth model
or expensive equipment severely limits their scalability to generic 3D
perception. In this paper, we propose a stereo-guided monocular 3D object
detection framework, dubbed SGM3D, adapting the robust 3D features learned from
stereo inputs to enhance the feature for monocular detection. We innovatively
present a multi-granularity domain adaptation (MG-DA) mechanism to exploit the
network's ability to generate stereo-mimicking features given only on monocular
cues. Coarse BEV feature-level, as well as the fine anchor-level domain
adaptation, are both leveraged for guidance in the monocular domain.In
addition, we introduce an IoU matching-based alignment (IoU-MA) method for
object-level domain adaptation between the stereo and monocular predictions to
alleviate the mismatches while adopting the MG-DA. Extensive experiments
demonstrate state-of-the-art results on KITTI and Lyft datasets.

本文提出了一种名为 SGM3D 的新方法，通过引入多粒度域自适应和基于 IoU 匹配的对齐方法，将立体 3D 特征适应到单眼检测中，显著提高了检测性能。实验证明，该方法在 KITTI 和 Lyft 数据集上取得了最先进的结果。