  Instance segmentation is an important task for scene understanding. Compared
to the fully-developed 2D, 3D instance segmentation for point clouds have much
room to improve. In this paper, we present PointGroup, a new end-to-end
bottom-up architecture, specifically focused on better grouping the points by
exploring the void space between objects. We design a two-branch network to
extract point features and predict semantic labels and offsets, for shifting
each point towards its respective instance centroid. A clustering component is
followed to utilize both the original and offset-shifted point coordinate sets,
taking advantage of their complementary strength. Further, we formulate the
ScoreNet to evaluate the candidate instances, followed by the Non-Maximum
Suppression (NMS) to remove duplicates. We conduct extensive experiments on two
challenging datasets, ScanNet v2 and S3DIS, on which our method achieves the
highest performance, 63.6% and 64.0%, compared to 54.9% and 54.4% achieved by
former best solutions in terms of mAP with IoU threshold 0.5.
