Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering
This work addresses the problem of efficient and accurate 3D scene understanding for applications like robotics and autonomous driving, representing an incremental improvement with novel efficiency aspects.
The paper tackles panoptic segmentation of large 3D point clouds by redefining it as a scalable graph clustering problem, achieving state-of-the-art performance with significant efficiency gains, such as a 50.1 PQ score on S3DIS Area 5 and a model 30 times smaller than competitors.
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.