  We propose a fully automatic annotation scheme that takes a raw 3D point
cloud with a set of fitted CAD models as input and outputs convincing
point-wise labels that can be used as cheap training data for point cloud
segmentation. Compared with manual annotations, we show that our automatic
labels are accurate while drastically reducing the annotation time and
eliminating the need for manual intervention or dataset-specific parameters.
Our labeling pipeline outputs semantic classes and soft point-wise object
scores, which can either be binarized into standard one-hot-encoded labels,
thresholded into weak labels with ambiguous points left unlabeled, or used
directly as soft labels during training. We evaluate the label quality and
segmentation performance of PointNet++ on a dataset of real industrial point
clouds and Scan2CAD, a public dataset of indoor scenes. Our results indicate
that reducing supervision in areas that are more difficult to label
automatically is beneficial compared with the conventional approach of naively
assigning a hard "best guess" label to every point.

Computer Vision and Pattern Recognition