Monocular 3D object detection has attracted great attention for its
advantages in simplicity and cost. Due to the ill-posed 2D to 3D mapping
essence from the monocular imaging process, monocular 3D object detection
suffers from inaccurate depth estimation and thus has poor 3D detection
results. To alleviate this problem, we propose to introduce the ground plane as
a prior in the monocular 3d object detection. The ground plane prior serves as
an additional geometric condition to the ill-posed mapping and an extra source
in depth estimation. In this way, we can get a more accurate depth estimation
from the ground. Meanwhile, to take full advantage of the ground plane prior,
we propose a depth-align training strategy and a precise two-stage depth
inference method tailored for the ground plane prior. It is worth noting that
the introduced ground plane prior requires no extra data sources like LiDAR,
stereo images, and depth information. Extensive experiments on the KITTI
benchmark show that our method could achieve state-of-the-art results compared
with other methods while maintaining a very fast speed. Our code and models are
available at this https URL

本文提出采用地面平面作为额外的几何条件来解决单目视觉下深度估计不准确的问题，同时采用深度校准和精确的两阶段深度推断方法，这种方法不需要额外的数据来源，能够在 KITTI 基准测试中取得最佳的结果，并保持非常快的速度。