MoiréLens: Bringing Schlieren Imaging into Real-World Environments Using Moiré Patterns

Airflows, heat plumes, and gas leaks are ubiquitous yet invisible to both the human eye and commodity cameras, limiting visual sensing of environmental dynamics critical to safety, comfort, and automation. Traditional Schlieren imaging reveals refractive-index variations but requires bulky, expensive optics and high-end cameras, limiting it to controlled lab settings. We present MoiréLens, a practical Schlieren imaging system that works in real-world environments by capturing Moiré interference with a low-cost embedded camera. By embedding high-frequency, human-imperceptible Moiré stimuli into everyday wallpaper, MoiréLens converts subtle refractive-index variations into measurable Moiré phase shifts formed between the background and the camera's color-filter array. An automatic calibration module maintains stable, high-sensitivity Moiré pattern generation under camera viewpoint misalignment by dynamically adjusting the displayed Moiré stimulus. A color Moiré extraction and Moiré-to-Schlieren conversion pipeline isolates Moiré patterns from wallpaper and reconstructs refractive-index variations with adaptive spatial-temporal sensitivity. Extensive experiments show that MoiréLens achieves high-sensitivity Schlieren imaging for both regional airflows and global plumes. Using a 1-megapixel camera, it localizes a 1.5 hPa gas leak at 0.8 m and butane from an unlit lighter at 1 m, extending the sensing range by 3× over background-oriented Schlieren. Leveraging heat-plume visualization, MoiréLens further classifies dry- and moist-heat cooking with 99.8% accuracy and estimates oil temperature with an average error of 7.10°C under unseen heating powers.