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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.05876 |
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| _version_ | 1866910013067362304 |
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| author | Yan, Chenyang Bengtsson, Mats |
| author_facet | Yan, Chenyang Bengtsson, Mats |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_05876 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | IDSOR: Intensity- and Distance-Aware Statistical Outlier Removal for Weather-Robust LiDAR Point Clouds Yan, Chenyang Bengtsson, Mats Signal Processing 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. |
| title | IDSOR: Intensity- and Distance-Aware Statistical Outlier Removal for Weather-Robust LiDAR Point Clouds |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2602.05876 |