Monocular 3D object detection is one of the most challenging tasks in 3D
scene understanding. Due to the ill-posed nature of monocular imagery, existing
monocular 3D detection methods highly rely on training with the manually
annotated 3D box labels on the LiDAR point clouds. This annotation process is
very laborious and expensive. To dispense with the reliance on 3D box labels,
in this paper we explore the weakly supervised monocular 3D detection.
Specifically, we first detect 2D boxes on the image. Then, we adopt the
generated 2D boxes to select corresponding RoI LiDAR points as the weak
supervision. Eventually, we adopt a network to predict 3D boxes which can
tightly align with associated RoI LiDAR points. This network is learned by
minimizing our newly-proposed 3D alignment loss between the 3D box estimates
and the corresponding RoI LiDAR points. We will illustrate the potential
challenges of the above learning problem and resolve these challenges by
introducing several effective designs into our method. Codes will be available
at this https URL

本文介绍了一种采用弱监督学习的单目三维目标检测方法，该方法利用生成的二维框选择对应的区域兴趣 LiDAR 点作为弱监督信号，并通过网络预测可以紧密对齐相应区域兴趣 LiDAR 点的三维框，从而消除了对三维框标注的依赖。