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
this https URL

介绍了一种高效的方法，通过将大型 3D 点云的全景分割任务重新定义为可扩展的图聚类问题来进行。该方法可以仅使用本地辅助任务进行训练，从而在训练过程中消除了资源密集型的实例匹配步骤。此外，我们的方法可以轻松适应 superpoint 范例，进一步提高其效率。这使得我们的模型能够在单次推理中处理具有数百万个点和数千个对象的场景。我们的方法（SuperCluster）在两个室内扫描数据集（S3DIS Area 5 的 PQ 指标为 50.1（+7.8），ScanNetV2 的 PQ 指标为 58.7（+25.2））上实现了全景分割的最新性能。此外，我们还在两个大规模移动映射基准测试（KITTI-360 和 DALES）中取得了首个最先进的结果。我们的模型仅有 209k 个参数，比最佳竞争方法小 30 倍，训练速度最高提高 15 倍。我们的代码和预训练模型可在此 https URL 获取。