Learning to Suppress SPAD-based LiDAR Flare

Single-Photon Avalanche Diode (SPAD)-based Light Detec-tion and Ranging (LiDAR) is emerging for autonomous vehicles due toits high sensitivity and precise depth sensing capabilities. However, flarecaused by excessive photon returns or pile-up effects can lead to incorrectdepth estimation and exaggerated boundaries in point clouds, resultingin severe distortions of geometric measurements, making flare suppres-sion essential for safety-critical applications. Existing flare mitigationmethods primarily operate at the hardware or signal-processing levels.While effective under specific configurations, they are largely rule-basedand configuration-dependent, lacking learnable representations that gen-eralize across diverse sensing scenarios. In this work, we reformulate flaresuppression as a semantic segmentation problem, enabling data-drivenlearning of geometric and photometric cues directly from SPAD mea-surements. We first benchmark representative segmentation models onthe newly introduced SPAD flare dataset and observe that they strug-gle to exploit the intrinsic multi-echo characteristics of SPAD signals.Motivated by this observation, we propose Physically-Informed seg-mentation for LiDAR Flare (PILF), a learning-based approach thattreats the first and second echoes, together with ambient illumination,as distinct modalities, aggregating cross-echo information while jointlyencoding geometric and photometric features. Experiments across mul-tiple real-world scenes demonstrate that PILF significantly outperformscompared segmentation models, achieving up to 79.32% mIoU, andproviding an effective solution for SPAD-based LiDAR flare suppression.