SuperiorGAT: Improving LiDAR Resolution with Graph Attention
Published:Dec 27, 2025 02:25
•1 min read
•ArXiv
Analysis
This paper addresses a practical problem in autonomous systems: the limitations of LiDAR sensors due to sparse data and occlusions. SuperiorGAT offers a computationally efficient solution by using a graph attention network to reconstruct missing elevation information. The focus on architectural refinement, rather than hardware upgrades, is a key advantage. The evaluation on diverse KITTI environments and comparison to established baselines strengthens the paper's claims.
Key Takeaways
- •Proposes SuperiorGAT, a graph attention-based framework for LiDAR point cloud reconstruction.
- •Addresses the problem of sparse LiDAR data and beam dropout.
- •Achieves improved performance compared to existing methods without increasing network depth.
- •Demonstrates effectiveness across diverse KITTI environments.
- •Offers a computationally efficient method for improving LiDAR resolution.
Reference
“SuperiorGAT consistently achieves lower reconstruction error and improved geometric consistency compared to PointNet-based models and deeper GAT baselines.”