Papers › What Can be Seen is What You Get: Structure Aware Point Cloud Augmentation
What Can be Seen is What You Get: Structure Aware Point Cloud Augmentation
Frederik Hasecke, Martin Alsfasser, Anton Kummert
To train a well performing neural network for semantic segmentation, it is crucial to have a large dataset with available ground truth for the network to generalize on unseen data. In this paper we present novel point cloud augmentation methods to artificially diversify a dataset. Our sensor-centric methods keep the data structure consistent with the lidar sensor capabilities. Due to these new methods, we are able to enrich low-value data with high-value instances, as well as create entirely new scenes. We validate our methods on multiple neural networks with the public SemanticKITTI dataset and demonstrate that all networks improve compared to their respective baseline. In addition, we show that our methods enable the use of very small datasets, saving annotation time, training time and the associated costs.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Semantic Segmentation | SemanticKITTI | SAPCA (Cylinder3D) | mIoU (1% Labels) | 50.9 | #5 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | SemanticKITTI | SAPCA (Cylinder3D) | mIoU (10% Labels) | 64.0 | #5 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | SemanticKITTI | SAPCA (Cylinder3D) | mIoU (50% Labels) | 64.9 | #5 of 12 | Archive leaderboard | report |
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