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.

WyPR是一种仅需要场景级别标签作为监督的弱监督点云识别框架，可联合解决点级语义分割、3D建议生成和3D目标检测等三个核心任务，通过自身和跨任务一致性损失耦合它们的预测。在多实例学习目标的辅助下，WyPR能在训练时不需要访问任何空间标签的情况下对点云数据中的物体进行检测和分割，并使用ScanNet和S3DIS数据集证明了其有效性，在弱监督分割方面优于先前的最新技术6％以上。此外，我们还在两个数据集上开展了弱监督3D目标检测的第一次基准测试，结果WyPR优于标准方法，并为未来的工作建立了强基础。

没有空间标记的3D空间识别