Simulating Refractive Distortions and Weather-Induced Artifacts for Resource-Constrained Autonomous Perception

• URL: http://arxiv.org/abs/2507.05536v1
• Date: Mon, 07 Jul 2025 23:21:19 GMT
• Title: Simulating Refractive Distortions and Weather-Induced Artifacts for Resource-Constrained Autonomous Perception
• Authors: Moseli Mots'oehli, Feimei Chen, Hok Wai Chan, Itumeleng Tlali, Thulani Babeli, Kyungim Baek, Huaijin Chen,
• Abstract summary: We present a procedural augmentation pipeline that enhances low-cost monocular dashcam footage with realistic refractive distortions and weather-induced artifacts.<n>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.<n>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.
• Score: 1.8463472137156713
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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