  We introduce WyPR, a Weakly-supervised framework for Point cloud Recognition,
requiring only scene-level class tags as supervision. WyPR jointly addresses
three core 3D recognition tasks: point-level semantic segmentation, 3D proposal
generation, and 3D object detection, coupling their predictions through self
and cross-task consistency losses. We show that in conjunction with standard
multiple-instance learning objectives, WyPR can detect and segment objects in
point cloud data without access to any spatial labels at training time. We
demonstrate its efficacy using the ScanNet and S3DIS datasets, outperforming
prior state of the art on weakly-supervised segmentation by more than 6% mIoU.
In addition, we set up the first benchmark for weakly-supervised 3D object
detection on both datasets, where WyPR outperforms standard approaches and
establishes strong baselines for future work.
