Efficient 3D Semantic Segmentation with Superpoint Transformer
This addresses the computational bottleneck in large-scale 3D scene understanding for applications like autonomous driving and robotics, with significant efficiency gains.
The paper tackles efficient 3D semantic segmentation by introducing a superpoint-based transformer architecture, achieving state-of-the-art performance on benchmarks like S3DIS (76.0% mIoU) while being up to 200 times more compact and 7 times faster in preprocessing than existing methods.
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-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.