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Conference Papers Year : 2023

Efficient 3D Semantic Segmentation with Superpoint Transformer


We introduce a novel superpoint-based transformer architecture for efficient semantic segmentation of large-scale 3D scenes. Our method incorporates a fast algorithm to partition point clouds into a hierarchical superpoint structure, which makes our preprocessing 7 times times faster than existing superpoint-based approaches. Additionally, we leverage a self-attention mechanism to capture the relationships between superpoints at multiple scales, leading to state-of-the-art performance on three challenging benchmark datasets: S3DIS (76.0% mIoU 6-fold validation), KITTI-360 (63.5% on Val), and DALES (79.6%). With only 212k parameters, our approach is up to 200 times more compact than other state-of-the-art models while maintaining similar performance. Furthermore, our model can be trained on a single GPU in 3 hours for a fold of the S3DIS dataset, which is 7x to 70x fewer GPU-hours than the best-performing methods. Our code and models are accessible at github.com/drprojects/superpoint_transformer.
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hal-04140052 , version 1 (24-06-2023)





Damien Robert, Hugo Raguet, Loic Landrieu. Efficient 3D Semantic Segmentation with Superpoint Transformer. International Conference on Computer Vision, CVR, IEEE, 2023, Paris, France. ⟨hal-04140052⟩
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