  Existing state-of-the-art 3D instance segmentation methods perform semantic
segmentation followed by grouping. The hard predictions are made when
performing semantic segmentation such that each point is associated with a
single class. However, the errors stemming from hard decision propagate into
grouping that results in (1) low overlaps between the predicted instance with
the ground truth and (2) substantial false positives. To address the
aforementioned problems, this paper proposes a 3D instance segmentation method
referred to as SoftGroup by performing bottom-up soft grouping followed by
top-down refinement. SoftGroup allows each point to be associated with multiple
classes to mitigate the problems stemming from semantic prediction errors and
suppresses false positive instances by learning to categorize them as
background. Experimental results on different datasets and multiple evaluation
metrics demonstrate the efficacy of SoftGroup. Its performance surpasses the
strongest prior method by a significant margin of +6.2% on the ScanNet v2
hidden test set and +6.8% on S3DIS Area 5 in terms of AP_50. SoftGroup is also
fast, running at 345ms per scan with a single Titan X on ScanNet v2 dataset.
The source code and trained models for both datasets are available at
\url{https://github.com/thangvubk/SoftGroup.git}.
