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.

翻译：我们提出一种高效的大规模三维点云全景分割方法，通过将该任务重新定义为可扩展的图聚类问题。该方法仅需利用局部辅助任务进行训练，从而消除了训练过程中资源密集型的实例匹配步骤。此外，我们的公式可轻松适配超点范式，进一步提升效率。这使得模型能够通过单次推理处理包含数百万点和数千对象的场景。我们提出的SuperCluster方法在两个室内扫描数据集上实现了最新的全景分割性能：S3DIS Area~5数据集达到$50.1$ PQ（提升$+7.8$），ScanNetV2数据集达到$58.7$ PQ（提升$+25.2$）。我们还在两个大规模移动测绘基准数据集KITTI-360和DALES上首次确立了最新性能。在仅拥有$209$k参数的情况下，我们的模型比最优竞争方法小$30$倍以上，训练速度快达$15$倍。我们的代码和预训练模型可访问https://github.com/drprojects/superpoint_transformer获取。