  Weakly supervised 3D object detection aims to learn a 3D detector with lower
annotation cost, e.g., 2D labels. Unlike prior work which still relies on few
accurate 3D annotations, we propose a framework to study how to leverage
constraints between 2D and 3D domains without requiring any 3D labels.
Specifically, we employ visual data from three perspectives to establish
connections between 2D and 3D domains. First, we design a feature-level
constraint to align LiDAR and image features based on object-aware regions.
Second, the output-level constraint is developed to enforce the overlap between
2D and projected 3D box estimations. Finally, the training-level constraint is
utilized by producing accurate and consistent 3D pseudo-labels that align with
the visual data. We conduct extensive experiments on the KITTI dataset to
validate the effectiveness of the proposed three constraints. Without using any
3D labels, our method achieves favorable performance against state-of-the-art
approaches and is competitive with the method that uses 500-frame 3D
annotations. Code and models will be made publicly available at
https://github.com/kuanchihhuang/VG-W3D.
