SuperiorGAT Enhances LiDAR Elevation Reconstruction in Autonomous Systems

Global: SuperiorGAT Enhances LiDAR Elevation Reconstruction in Autonomous Systems

A novel graph attention framework called SuperiorGAT has been presented to address the limited vertical resolution of LiDAR sensors in autonomous platforms. By reconstructing missing elevation data in sparse point clouds, the approach aims to improve perception accuracy without adding hardware complexity.

Methodology

The researchers model LiDAR scans as beam‑aware graphs, allowing each vertical scanning line to be treated as a node within a network. SuperiorGAT incorporates gated residual fusion and a feed‑forward refinement stage, enabling the system to recover omitted beams while maintaining a shallow network depth.

Evaluation Setup

To simulate realistic sensor degradation, the study removes every fourth vertical scanning beam, creating a structured beam dropout scenario. The framework is then tested across multiple KITTI subsets—including Person, Road, Campus, and City sequences—to gauge performance under diverse environmental conditions.

Results

Across all test scenarios, SuperiorGAT consistently yields lower reconstruction error and stronger geometric consistency than comparable PointNet‑based models and deeper graph attention baselines. Qualitative X‑Z projections illustrate that the reconstructed point clouds preserve structural integrity with minimal vertical distortion.

Implications

The findings suggest that architectural refinements can serve as a computationally efficient alternative to hardware upgrades for enhancing LiDAR resolution. By improving vertical beam fidelity, autonomous systems may achieve more reliable object detection and scene understanding.

Future work could explore integration with real‑time perception pipelines and assess the framework’s robustness to dynamic occlusions in outdoor environments.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.