LiDAR-based perception in autonomous systems is constrained by fixed vertical beam resolution and further compromised by beam dropout resulting from environmental occlusions. This paper introduces SuperiorGAT, a graph attention-based framework designed to reconstruct missing elevation information in sparse LiDAR point clouds. By modeling LiDAR scans as beam-aware graphs and incorporating gated residual fusion with feed-forward refinement, SuperiorGAT enables accurate reconstruction without increasing network depth. To evaluate performance, structured beam dropout is simulated by removing every fourth vertical scanning beam. Extensive experiments across diverse KITTI environments, including Person, Road, Campus, and City sequences, demonstrate that SuperiorGAT consistently achieves lower reconstruction error and improved geometric consistency compared to PointNet-based models and deeper GAT baselines. Qualitative X-Z projections further confirm the model's ability to preserve structural integrity with minimal vertical distortion. These results suggest that architectural refinement offers a computationally efficient method for improving LiDAR resolution without requiring additional sensor hardware.

翻译：自主系统中基于LiDAR的感知受限于固定的垂直波束分辨率，并因环境遮挡导致的波束丢失而进一步受限。本文提出SuperiorGAT，一种基于图注意力的框架，旨在重建稀疏LiDAR点云中缺失的高程信息。通过将LiDAR扫描建模为波束感知图，并结合门控残差融合与前馈细化机制，SuperiorGAT能够在无需增加网络深度的前提下实现精确重建。为评估性能，通过移除每第四条垂直扫描波束来模拟结构化波束丢失。在包括Person、Road、Campus和City序列在内的多种KITTI环境中的大量实验表明，与基于PointNet的模型及更深的GAT基线相比，SuperiorGAT始终实现更低的重建误差与更优的几何一致性。定性的X-Z投影进一步证实了该模型能以最小的垂直畸变保持结构完整性的能力。这些结果表明，架构优化提供了一种计算高效的方法，可在无需额外传感器硬件的情况下提升LiDAR分辨率。