# Neural Reconstruction of LiDAR Point Clouds under Jamming Attacks via Full-Waveform Representation and Simultaneous Laser Sensing Source: [Neural Reconstruction of LiDAR Point Clouds under Jamming Attacks via Full-Waveform Representation and Simultaneous Laser Sensing]() Collection index: [llms.txt]() Data updated: 2026-04-03T05:00:04.568Z Authors: Ryo Yoshida, Takami Sato, Wenlun Zhang, Yuki Hayakawa, Shota Nagai, Takahiro Kado, Taro Beppu, Ibuki Fujioka, Yunshan Zhong, Kentaro Yoshioka Published: 2026-04-01T01:37:11.000Z ## Abstract LiDAR sensors are critical for autonomous driving perception, yet remain vulnerable to spoofing attacks. Jamming attacks inject high-frequency laser pulses that completely blind LiDAR sensors by overwhelming authentic returns with malicious signals. We discover that while point clouds become randomized, the underlying full-waveform data retains distinguishable signatures between attack and legitimate signals. In this work, we propose PULSAR-Net, capable of reconstructing authentic point clouds under jamming attacks by leveraging previously underutilized intermediate full-waveform representations and simultaneous laser sensing in modern LiDAR systems. PULSAR-Net adopts a novel U-Net architecture with axial spatial attention mechanisms specifically designed to identify attack-induced signals from authentic object returns in the full-waveform representation. To address the lack of full-waveform representations in existing LiDAR datasets under jamming attacks, we introduce a physics-aware dataset generation pipeline that synthesizes realistic full-waveform representations under jamming attacks. Despite being trained exclusively on synthetic data, PULSAR-Net achieves reconstruction rates of 92% and 73% for vehicles obscured by jamming attacks in real-world static and driving scenarios, respectively. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Point-level recovery on nuScenes-J](): Point-level recovery; nuScenes J; Point-level Recovery Accuracy: 86.75. - [Point-level recovery on KITTI-J](): Point-level recovery; KITTI-J; Point-level Recovery Accuracy: 89.9. - [3D Object Detection on nuScenes 4 Static Scenarios](): 3D Object Detection; nuScenes 4 Static Scenarios; mAP: 69.4. - [3D Object Detection on nuScenes 3 moving scenarios](): 3D Object Detection; nuScenes 3 moving scenarios; mAP: 63.1. - [3D Object Detection on nuScenes J](): 3D Object Detection; nuScenes J; mAP: 65.52. - [3D Object Detection on KITTI-J](): 3D Object Detection; KITTI-J; mAP: 56.67. ## Original sources and implementation [Original paper]() [PDF]()