Efficient 3D Semantic Segmentation with Superpoint Transformer (2306.08045v2)

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-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.

• The paper introduces a novel hierarchical superpoint partitioning method that accelerates segmentation by 7× while preserving local geometric and radiometric features.
• Its transformer architecture utilizes a sparse self-attention mechanism to capture multi-scale contextual relationships in extensive 3D scenes.
• The paper demonstrates that the lightweight model, with only 212k parameters and a 200× size reduction, achieves competitive benchmark performance with reduced training time.

Efficient 3D Semantic Segmentation with Superpoint Transformers

The paper introduces a novel approach for 3D semantic segmentation, focusing on an efficient architecture termed as \METHOD (Superpoint Transformer). The method leverages a hierarchical superpoint structure alongside a transformer network to improve large-scale 3D scene segmentation both in performance and efficiency.

Key Contributions

1. Hierarchical Superpoint Partitioning: The proposed method utilizes a rapid preprocessing algorithm to segment point clouds into hierarchical superpoints. It achieves a $7\times$ acceleration compared to existing superpoint methods. This preprocessing aligns with the local geometric and radiometric properties, enhancing stability and reducing computational overhead.
2. Transformer Architecture: \METHOD employs a self-attention mechanism, capturing contextual relationships between superpoints over multiple scales. This sparse attention scheme enables the model to process extensive 3D scenes effectively while maintaining state-of-the-art accuracy.
3. Efficiency: Despite achieving comparable performance on benchmarks like S3DIS, KITTI-360, and DALES, the model exhibits remarkable compaction with only $212$k parameters. It registers up to $200\times$ reduction in model size compared to contemporaries while being substantially faster—requiring only a fraction of the GPU hours.

Strong Numerical Results

• Performance on Benchmarks:

With results such as $76.0\%$ mIoU on S3DIS (6-fold validation), the model demonstrates its capability, even outperforming more complex models in some scenarios.

• Resource Efficiency:

It reports drastic reductions in training time—taking $3$ hours per fold on a single GPU for the S3DIS dataset, considerably less than competitive methods.

Implications and Future Directions

The implications of this research extend across both theoretical and practical fronts. Theoretically, it suggests a shift towards more adaptive data partitioning within neural network frameworks, emphasizing the balance between model complexity and performance. Practically, the reduction in training time and resource requirements aligns with industry needs for deployable AI solutions in resource-constrained environments.

Future exploration could explore the integration of learned features for partitioning the superpoints, potentially improving boundaries within ambiguous regions without significant preprocessing delays. The scalability of such models could also be tested further with even larger datasets or real-time applications.

Conclusion

This research provides significant improvements in efficiency for 3D scene segmentation, emphasizing a tailored, lightweight approach. The insights around hierarchical segmentation and sparse attention mechanisms present a valuable direction for future advancements in vision transformers and 3D semantic segmentation.