  Most existing point cloud instance and semantic segmentation methods rely
heavily on strong supervision signals, which require point-level labels for
every point in the scene. However, such strong supervision suffers from large
annotation costs, arousing the need to study efficient annotating. In this
paper, we discover that the locations of instances matter for both instance and
semantic 3D scene segmentation. By fully taking advantage of locations, we
design a weakly-supervised point cloud segmentation method that only requires
clicking on one point per instance to indicate its location for annotation.
With over-segmentation for pre-processing, we extend these location annotations
into segments as seg-level labels. We further design a segment grouping network
(SegGroup) to generate point-level pseudo labels under seg-level labels by
hierarchically grouping the unlabeled segments into the relevant nearby labeled
segments, so that existing point-level supervised segmentation models can
directly consume these pseudo labels for training. Experimental results show
that our seg-level supervised method (SegGroup) achieves comparable results
with the fully annotated point-level supervised methods. Moreover, it
outperforms the recent weakly-supervised methods given a fixed annotation
budget. Code is available at https://github.com/AnTao97/SegGroup.

Computer Vision and Pattern Recognition