PULSAR-Net, the first effective defense against LiDAR jamming attacks that blind sensors by flooding them with high-frequency laser pulses. By leveraging intermediate full-waveform data (normally discarded after peak detection) and simultaneous multi-laser sensing patterns, a 3D U-Net with axial spatial-temporal attention segments attack pulses from legitimate reflections directly in the waveform domain. Trained exclusively on synthetic data, PULSAR-Net achieves 92% and 73% point-cloud reconstruction rates for vehicles erased by attacks in real-world static and driving scenarios respectively.