用图注意力网络修复自动驾驶中稀疏激光雷达的缺失高度信息
SuperiorGAT: Graph Attention Networks for Sparse LiDAR Point Cloud Reconstruction in Autonomous Systems
- 将激光雷达扫描建模为带束感知的图结构，结合门控残差融合与前馈精修
- 在移除每第4条垂直扫描束的模拟测试中，重建误差显著低于点云基线模型
- 无需增加网络深度即可提升分辨率，适合资源受限的车载系统
自动驾驶中的激光雷达感知受固定垂直束分辨率限制，且环境遮挡导致束丢失。本文提出SuperiorGAT，一种基于图注意力的框架，用于重建稀疏激光雷达点云中的缺失高程信息。通过将激光雷达扫描建模为束感知图，并引入门控残差融合与前馈精修机制，实现高精度重建而无需增加网络深度。实验中采用模拟结构化束丢失（每第4条垂直扫描束被移除）进行评估，在KITTI数据集的Person、Road、Campus和City序列上，SuperiorGAT始终优于基于PointNet的模型及更深的GAT基线，重建误差更低，几何一致性更优。定性分析显示，其在X-Z投影中能有效保持结构完整性，垂直失真极小。结果表明，架构优化可不依赖额外硬件即实现高效分辨率提升。
原文摘要 · Abstract (English)
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.
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