  We propose a point cloud annotation framework that employs human-in-loop
learning to enable the creation of large point cloud datasets with per-point
annotations. Sparse labels from a human annotator are iteratively propagated to
generate a full segmentation of the network by fine-tuning a pre-trained model
of an allied task via a few-shot learning paradigm. We show that the proposed
framework significantly reduces the amount of human interaction needed in
annotating point clouds, without sacrificing on the quality of the annotations.
Our experiments also suggest the suitability of the framework in annotating
large datasets by noting a reduction in human interaction as the number of full
annotations completed by the system increases. Finally, we demonstrate the
flexibility of the framework to support multiple different annotations of the
same point cloud enabling the creation of datasets with different granularities
of annotation.

Computer Vision and Pattern Recognition;Machine Learning