GitHub repository

Models have as few as 212k parameters, making them 200x smaller than PointNeXt and 40x smaller than Stratified Transformer, as highlighted in the README benchmarks.

SPT trains on S3DIS in 3 hours on one GPU, with preprocessing 7x faster than Superpoint Graph, and SuperCluster processes 18M points in 10.1s, enabling large-scale processing.

SuperCluster formulates panoptic segmentation as superpoint graph clustering, allowing it to handle massive scenes on a single GPU with fewer than 1M parameters, per the paper results.

Achieves state-of-the-art mIoU and PQ scores on datasets like S3DIS, KITTI-360, and DALES, with results backed by Papers with Code badges in the README.

Includes tools for creating shareable HTML visualizations of 3D segmentation results, enhancing interpretability and collaboration, as shown in the notebooks and media.