Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering (2401.06704v2)

Abstract: 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.

• The paper formulates 3D panoptic segmentation as a graph clustering problem to overcome memory limits and enable unlimited object detection.
• The paper introduces local auxiliary tasks that eliminate resource-intensive instance-matching, significantly reducing training complexity and duration.
• The paper demonstrates state-of-the-art performance with improvements of 7.8 and 25.2 PQ points on S3DIS and ScanNetV2, using just 209k parameters.

SuperCluster: Scalable Panoptic Segmentation for Large 3D Point Clouds

The paper presents SuperCluster, an efficient approach for panoptic segmentation, particularly focused on large 3D point clouds. This method redefines the segmentation task as a scalable graph clustering problem, leveraging local auxiliary tasks during training, and integrating with the superpoint paradigm for increased processing efficiency.

Technological Context and Motivation

Panoptic segmentation in large-scale 3D environments is crucial for numerous applications, such as digital twins and city digitization. However, existing methods often target smaller scenes due to substantial memory and computational constraints. Large-scale 3D analysis is mostly unexplored due to the significant challenges, including the diversity of objects and memory-intensive operations required by traditional approaches.

Core Contributions

1. Graph-Based Clustering for Segmentation: The paper formulates 3D panoptic segmentation as a graph clustering problem, where the segmentation task is solved by clustering a graph representing point cloud adjacency. This method eliminates a fixed limit on detectable objects, improving scalability.
2. Local Supervision for Training: By employing local auxiliary tasks, SuperCluster avoids the need for resource-intensive instance-matching, significantly reducing training complexity and duration.
3. Superpoint Paradigm: The method applies its framework to the superpoint paradigm, computing predictions and conducting supervision entirely at the superpoint level. This approach dramatically reduces the complexity compared to operating on individual points, enabling the handling of much larger scenes.

Performance and Results

SuperCluster demonstrates state-of-the-art results in panoptic segmentation across several datasets:

• S3DIS Area 5: Achieves a 50.1 PQ, marking a notable improvement of 7.8 points.
• ScanNetV2: Attains a PQ of 58.7, surpassing previous methods by 25.2 points.

These results highlight that SuperCluster is not only efficient in terms of parameter size (209k parameters, over 30 times smaller than competing methods) but also in terms of processing speed, training up to 15 times faster than previous methods.

Implications and Future Directions

The implications of this research are significant for the field of 3D panoptic segmentation. SuperCluster's ability to easily scale and efficiently process large point clouds could enhance the automation of mapping and digitization tasks across various industries.

Future directions could include extending the algorithm to handle different types of point cloud data, optimizing the model for even larger environments, or integrating more complex neural architectures to further improve segmentation accuracy and robustness.

Conclusion

In conclusion, SuperCluster represents a pivotal advancement in the domain of 3D panoptic segmentation. Its innovative approach in reformulating segmentation tasks, coupled with its efficiency and scalability, sets a new benchmark in the field, paving the way for broader adoption and exploration in large-scale applications.