Abstract
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 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-theart 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 7× to 70× fewer GPU-hours than the best-performing methods.
- Task: 3D semantic segmentation
- Problem Definition: Not mentioned / For fast computation
- Approach: superpoint / self-attention