  Most existing methods realize 3D instance segmentation by extending those
models used for 3D object detection or 3D semantic segmentation. However, these
non-straightforward methods suffer from two drawbacks: 1) Imprecise bounding
boxes or unsatisfactory semantic predictions limit the performance of the
overall 3D instance segmentation framework. 2) Existing method requires a
time-consuming intermediate step of aggregation. To address these issues, this
paper proposes a novel end-to-end 3D instance segmentation method based on
Superpoint Transformer, named as SPFormer. It groups potential features from
point clouds into superpoints, and directly predicts instances through query
vectors without relying on the results of object detection or semantic
segmentation. The key step in this framework is a novel query decoder with
transformers that can capture the instance information through the superpoint
cross-attention mechanism and generate the superpoint masks of the instances.
Through bipartite matching based on superpoint masks, SPFormer can implement
the network training without the intermediate aggregation step, which
accelerates the network. Extensive experiments on ScanNetv2 and S3DIS
benchmarks verify that our method is concise yet efficient. Notably, SPFormer
exceeds compared state-of-the-art methods by 4.3% on ScanNetv2 hidden test set
in terms of mAP and keeps fast inference speed (247ms per frame)
simultaneously. Code is available at https://github.com/sunjiahao1999/SPFormer.
