  Instance segmentation in 3D scenes is fundamental in many applications of
scene understanding. It is yet challenging due to the compound factors of data
irregularity and uncertainty in the numbers of instances. State-of-the-art
methods largely rely on a general pipeline that first learns point-wise
features discriminative at semantic and instance levels, followed by a separate
step of point grouping for proposing object instances. While promising, they
have the shortcomings that (1) the second step is not supervised by the main
objective of instance segmentation, and (2) their point-wise feature learning
and grouping are less effective to deal with data irregularities, possibly
resulting in fragmented segmentations. To address these issues, we propose in
this work an end-to-end solution of Semantic Superpoint Tree Network (SSTNet)
for proposing object instances from scene points. Key in SSTNet is an
intermediate, semantic superpoint tree (SST), which is constructed based on the
learned semantic features of superpoints, and which will be traversed and split
at intermediate tree nodes for proposals of object instances. We also design in
SSTNet a refinement module, termed CliqueNet, to prune superpoints that may be
wrongly grouped into instance proposals. Experiments on the benchmarks of
ScanNet and S3DIS show the efficacy of our proposed method. At the time of
submission, SSTNet ranks top on the ScanNet (V2) leaderboard, with 2% higher of
mAP than the second best method. The source code in PyTorch is available at
https://github.com/Gorilla-Lab-SCUT/SSTNet.
