Monocular 3D object detection has become a mainstream approach in automatic driving for its easy application. A prominent advantage is that it does not need LiDAR point clouds during the inference. However, most current methods still rely on 3D point cloud data for labeling the ground truths used in the training phase. This inconsistency between the training and inference makes it hard to utilize the large-scale feedback data and increases the data collection expenses. To bridge this gap, we propose a new weakly supervised monocular 3D objection detection method, which can train the model with only 2D labels marked on images. To be specific, we explore three types of consistency in this task, i.e. the projection, multi-view and direction consistency, and design a weakly-supervised architecture based on these consistencies. Moreover, we propose a new 2D direction labeling method in this task to guide the model for accurate rotation direction prediction. Experiments show that our weakly-supervised method achieves comparable performance with some fully supervised methods. When used as a pre-training method, our model can significantly outperform the corresponding fully-supervised baseline with only 1/3 3D labels. https://github.com/weakmono3d/weakmono3d

翻译：单目3D目标检测因其易于部署而成为自动驾驶领域的主流方法。其显著优势在于推理过程中无需激光雷达点云数据。然而，现有大多数方法在训练阶段仍依赖3D点云数据进行真实标注，这种训练与推理之间的不一致性不仅导致大规模反馈数据难以利用，还增加了数据采集成本。为解决这一矛盾，本文提出一种新的弱监督单目3D目标检测方法，仅需图像上的2D标签即可完成模型训练。具体而言，我们探索了该任务中的三类一致性约束——投影一致性、多视角一致性与方向一致性，并基于这些约束设计了一种弱监督架构。此外，我们提出一种新的2D方向标注方法，用于引导模型实现精准的旋转方向预测。实验表明，本文弱监督方法在某些指标上可与全监督方法相媲美。当将该方法作为预训练策略时，模型仅需1/3的3D标签即可显著超越对应全监督基线。https://github.com/weakmono3d/weakmono3d