SuperiorGAT: Graph Attention Networks for Sparse LiDAR Point Cloud Reconstruction in Autonomous Systems

2025-12-26

AI Summary

To address height information sparsity in LiDAR point clouds caused by fixed vertical beam resolution and environmental occlusion, this paper proposes a lightweight and efficient elevation reconstruction method. The approach employs a graph attention network (GAT) for end-to-end learning. Its key contributions are: (1) a Beam-Aware Graph that explicitly encodes LiDAR scanning geometry; (2) a gated residual fusion mechanism enabling adaptive multi-scale feature aggregation without increasing network depth; and (3) a feed-forward refinement module to enhance geometric consistency. Evaluated on diverse KITTI scenes, the method significantly outperforms PointNet and deep GAT baselines. X-Z projection visualizations demonstrate superior structural integrity and minimal vertical distortion. Quantitatively, it reduces reconstruction error by up to 18.7%.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Reconstructs missing elevation in sparse LiDAR point clouds
• Addresses beam dropout from environmental occlusions in autonomous systems
• Improves LiDAR resolution without additional hardware or deeper networks

Innovation

• Graph attention networks reconstruct sparse LiDAR data
• Beam-aware graphs with gated fusion refine point clouds
• Architectural refinement improves resolution without extra hardware