  We introduce a highly efficient method for panoptic segmentation of large 3D
point clouds by redefining this task as a scalable graph clustering problem.
This approach can be trained using only local auxiliary tasks, thereby
eliminating the resource-intensive instance-matching step during training.
Moreover, our formulation can easily be adapted to the superpoint paradigm,
further increasing its efficiency. This allows our model to process scenes with
millions of points and thousands of objects in a single inference. Our method,
called SuperCluster, achieves a new state-of-the-art panoptic segmentation
performance for two indoor scanning datasets: $50.1$ PQ ($+7.8$) for S3DIS
Area~5, and $58.7$ PQ ($+25.2$) for ScanNetV2. We also set the first
state-of-the-art for two large-scale mobile mapping benchmarks: KITTI-360 and
DALES. With only $209$k parameters, our model is over $30$ times smaller than
the best-competing method and trains up to $15$ times faster. Our code and
pretrained models are available at
https://github.com/drprojects/superpoint_transformer.
