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