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.

本篇论文介绍一种新的基于超像素点的变压器架构，用于高效地语义分割大规模 3D 场景。