DHE-Net: Dual-Encoder Hierarchical Network for Real-World Low-Cost LiDAR Point Cloud Denoising

Light detection and ranging (LiDAR) point cloud denoising is critical for reliable environmental perception in autonomous driving and robotics. To overcome the lack of real-noise datasets and the limited generalization of algorithms that rely on synthetic data, we construct a real-world LiDAR denoising dataset with noise-clean pairs, named RealLiD. Meanwhile, we propose a dual-heterogeneous-encoder network (DHE-Net) tailored for real-world noise. DHE-Net leverages spatial order information obtained from Knearest neighbor (KNN) sampling. It employs heterogeneous dual encoders to extract both the central semantic details and boundary distribution features of point cloud patches, thereby enabling more effective denoising. Experiments on RealLiD demonstrate that DHE-Net substantially outperforms mainstream denoising algorithms across multiple metrics, including chamfer distance, thereby proving its robustness and practicality under real-world noise conditions. The dataset and code will be released as open-source after publication to support future research.