  Point clouds provide intrinsic geometric information and surface context for
scene understanding. Existing methods for point cloud segmentation require a
large amount of fully labeled data. Using advanced depth sensors, collection of
large scale 3D dataset is no longer a cumbersome process. However, manually
producing point-level label on the large scale dataset is time and
labor-intensive. In this paper, we propose a weakly supervised approach to
predict point-level results using weak labels on 3D point clouds. We introduce
our multi-path region mining module to generate pseudo point-level label from a
classification network trained with weak labels. It mines the localization cues
for each class from various aspects of the network feature using different
attention modules. Then, we use the point-level pseudo labels to train a point
cloud segmentation network in a fully supervised manner. To the best of our
knowledge, this is the first method that uses cloud-level weak labels on raw 3D
space to train a point cloud semantic segmentation network. In our setting, the
3D weak labels only indicate the classes that appeared in our input sample. We
discuss both scene- and subcloud-level weakly labels on raw 3D point cloud data
and perform in-depth experiments on them. On ScanNet dataset, our result
trained with subcloud-level labels is compatible with some fully supervised
methods.
