Simulating Refractive Distortions and Weather-Induced Artifacts for Resource-Constrained Autonomous Perception
Journal:
arXiv
Published Date:
Jul 7, 2025
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.
