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

TL;DR

This paper introduces a procedural augmentation pipeline that simulates realistic refractive and weather artifacts in low-cost dashcam footage to improve autonomous perception in resource-constrained African environments.

Contribution

It presents a novel augmentation toolkit and benchmark dataset tailored for African driving scenarios, addressing data scarcity in low-resource settings.

Findings

Baseline image restoration models evaluated on augmented data.

Toolkit and dataset publicly released for research use.

Enhanced realism in simulated distortions improves perception robustness.

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.