Simulating Refractive Distortions and Weather-Induced Artifacts for Resource-Constrained Autonomous Perception
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916831888932864 |
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| author | Mots'oehli, Moseli Chen, Feimei Chan, Hok Wai Tlali, Itumeleng Babeli, Thulani Baek, Kyungim Chen, Huaijin |
| author_facet | Mots'oehli, Moseli Chen, Feimei Chan, Hok Wai Tlali, Itumeleng Babeli, Thulani Baek, Kyungim Chen, Huaijin |
| contents | The scarcity of autonomous vehicle datasets from developing regions, particularly across Africa's diverse urban, rural, and unpaved roads, remains a key obstacle to robust perception in low-resource settings. We present a procedural augmentation pipeline that enhances low-cost monocular dashcam footage with realistic refractive distortions and weather-induced artifacts tailored to challenging African driving scenarios. Our refractive module simulates optical effects from low-quality lenses and air turbulence, including lens distortion, Perlin noise, Thin-Plate Spline (TPS), and divergence-free (incompressible) warps. The weather module adds homogeneous fog, heterogeneous fog, and lens flare. To establish a benchmark, we provide baseline performance using three image restoration models. To support perception research in underrepresented African contexts, without costly data collection, labeling, or simulation, we release our distortion toolkit, augmented dataset splits, and benchmark results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05536 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Simulating Refractive Distortions and Weather-Induced Artifacts for Resource-Constrained Autonomous Perception Mots'oehli, Moseli Chen, Feimei Chan, Hok Wai Tlali, Itumeleng Babeli, Thulani Baek, Kyungim Chen, Huaijin Computer Vision and Pattern Recognition Emerging Technologies Machine Learning The scarcity of autonomous vehicle datasets from developing regions, particularly across Africa's diverse urban, rural, and unpaved roads, remains a key obstacle to robust perception in low-resource settings. We present a procedural augmentation pipeline that enhances low-cost monocular dashcam footage with realistic refractive distortions and weather-induced artifacts tailored to challenging African driving scenarios. Our refractive module simulates optical effects from low-quality lenses and air turbulence, including lens distortion, Perlin noise, Thin-Plate Spline (TPS), and divergence-free (incompressible) warps. The weather module adds homogeneous fog, heterogeneous fog, and lens flare. To establish a benchmark, we provide baseline performance using three image restoration models. To support perception research in underrepresented African contexts, without costly data collection, labeling, or simulation, we release our distortion toolkit, augmented dataset splits, and benchmark results. |
| title | Simulating Refractive Distortions and Weather-Induced Artifacts for Resource-Constrained Autonomous Perception |
| topic | Computer Vision and Pattern Recognition Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2507.05536 |