IDSOR: Intensity- and Distance-Aware Statistical Outlier Removal for Weather-Robust LiDAR Point Clouds

Abstract

LiDAR point clouds captured in rain or snow are often corrupted by weather-induced returns, which can degrade perception and safety-critical scene understanding. This paper proposes Intensity- and Distance-Aware Statistical Outlier Removal (IDSOR), a range-adaptive filtering method that jointly exploits intensity cues and neighborhood sparsity. By incorporating an empirical, range-dependent distribution of weather returns into the threshold design, IDSOR suppresses weather-induced points while preserving fine structural details without cumbersome manual parameter tuning. We also propose a variant that uses a previously proposed method to estimate the weather return distribution from data, and integrates it into IDSOR. Experiments on simulation-augmented level-crossing measurements and on the Winter Adverse Driving dataset (WADS) demonstrate that IDSOR achieves a favorable precision-recall trade-off, maintaining both precision and recall above 90% on WADS.