Binary Stripe Unwrapping Based on Mean-Speed Walk and Local Median Correction for Rapid High-Resolution Structured-Light Range Imaging

Structured light is frequently selected for efficient and accurate depth imaging, and single-frame-based methods have been presented for real-time sensing or imaging dynamic objects. However, many existing single-frame-based methods do not provide sufficient range resolution. Even those capable of sufficient range resolution mostly result in insufficient signal-to-noise ratio or depend on spatially windowed uniqueness, where a larger window makes the identification trickier. This paper presents a novel method for rapid structured-light range sensing using a binary color stripe pattern. For accurate and reliable depth acquisition, we identify projected stripes by our stripe segmentation and unwrapping algorithms. For robust stripe detection, the color-stripe segmentation algorithm performs image upsizing, motion blurring, and color balancing. The binary stripe unwrapping algorithm consists of mean-speed walk unrolling, row-wise unrolling, and local median correction, and resolves the high-frequency color-stripe redundancy efficiently and reliably. We provide a novel method for rapid structured-light range sensing using a binary color stripe pattern. Experimental results show the effectiveness and reliability of the presented method. Even using an entry-level phone camera under a low-cost DLP projector produces high-accuracy results.