  A crucial task in scene understanding is 3D object detection, which aims to
detect and localize the 3D bounding boxes of objects belonging to specific
classes. Existing 3D object detectors heavily rely on annotated 3D bounding
boxes during training, while these annotations could be expensive to obtain and
only accessible in limited scenarios. Weakly supervised learning is a promising
approach to reducing the annotation requirement, but existing weakly supervised
object detectors are mostly for 2D detection rather than 3D. In this work, we
propose VS3D, a framework for weakly supervised 3D object detection from point
clouds without using any ground truth 3D bounding box for training. First, we
introduce an unsupervised 3D proposal module that generates object proposals by
leveraging normalized point cloud densities. Second, we present a cross-modal
knowledge distillation strategy, where a convolutional neural network learns to
predict the final results from the 3D object proposals by querying a teacher
network pretrained on image datasets. Comprehensive experiments on the
challenging KITTI dataset demonstrate the superior performance of our VS3D in
diverse evaluation settings. The source code and pretrained models are publicly
available at
https://github.com/Zengyi-Qin/Weakly-Supervised-3D-Object-Detection.
