Point cloud segmentation with scene-level annotations is a promising but challenging task. Currently, the most popular way is to employ the class activation map (CAM) to locate discriminative regions and then generate point-level pseudo labels from scene-level annotations. However, these methods always suffer from the point imbalance among categories, as well as the sparse and incomplete supervision from CAM. In this paper, we propose a novel weighted hypergraph convolutional network-based method, called WHCN, to confront the challenges of learning point-wise labels from scene-level annotations. Firstly, in order to simultaneously overcome the point imbalance among different categories and reduce the model complexity, superpoints of a training point cloud are generated by exploiting the geometrically homogeneous partition. Then, a hypergraph is constructed based on the high-confidence superpoint-level seeds which are converted from scene-level annotations. Secondly, the WHCN takes the hypergraph as input and learns to predict high-precision point-level pseudo labels by label propagation. Besides the backbone network consisting of spectral hypergraph convolution blocks, a hyperedge attention module is learned to adjust the weights of hyperedges in the WHCN. Finally, a segmentation network is trained by these pseudo point cloud labels. We comprehensively conduct experiments on the ScanNet and S3DIS segmentation datasets. Experimental results demonstrate that the proposed WHCN is effective to predict the point labels with scene annotations, and yields state-of-the-art results in the community. The source code is available at http://zhiyongsu.github.io/Project/WHCN.html.

翻译：目前,最流行的方法是使用班级激活图(CAM)来定位有区别的区域,然后从场点的注解中生成点级假标签。然而,这些方法总是由于类别之间的点分布不平衡以及CAM的零和不完整监督而受到影响。在本文中,我们提出一种新型的加权超光速连接网络方法,称为WHCN,以迎接从场点的注解中学习点性标签的挑战。首先,为了同时克服不同类别之间的点分布不平衡并降低模型复杂性,利用几何等同质分布来生成一个培训点云的超级点。然后,根据从场点的注解中转换出来的高信任超点种子来构建一个高点的超点种子。第二,WHCN将高空超光速连接作为投入,并学习通过标签传播来预测高精度点的假标签。除了由光谱超光谱的相向相位结构块构成的骨干网络外,一个高端的CN关注模块,通过利用几度同质线点利用测平质线的网络分析结果来调整高点。SHEMalalalalalalalalalalalal 3 。最后,这是Syalalalalalalalalalalalalalalalalalalalalal 的计算结果。Syal 。Syalaldalalald 。Shexxxxxxxxxxxx。Shlus。Shlalalalalalalxxxxx。