🤖 AI Summary
To address the fundamental trade-off among resolution, accuracy, and frame rate in high-speed dynamic 3D scanning, this paper proposes a triangulation-free, data-driven structured-light 3D reconstruction method. Leveraging a linear translation stage, we acquire a high-density stack of checkerboard calibration images to construct a pixel-wise color-to-depth lookup table (LUT), which implicitly encodes optical distortions, sensor nonlinearities, and other system-level errors—requiring no hardware modification. By abandoning explicit geometric modeling, the approach enables end-to-end mapping learning directly from intensity patterns to depth. Experimental results demonstrate that the system achieves 500 fps at 1 MP resolution, with reconstruction accuracy and robustness significantly surpassing state-of-the-art commercial and open-source solutions. Moreover, it supports plug-and-play deployment without system retrofitting.
📝 Abstract
We introduce a novel calibration and reconstruction procedure for structured light scanning that foregoes explicit point triangulation in favor of a data-driven lookup procedure. The key idea is to sweep a calibration checkerboard over the entire scanning volume with a linear stage and acquire a dense stack of images to build a per-pixel lookup table from colors to depths. Imperfections in the setup, lens distortion, and sensor defects are baked into the calibration data, leading to a more reliable and accurate reconstruction. Existing structured light scanners can be reused without modifications while enjoying the superior precision and resilience that our calibration and reconstruction algorithms offer. Our algorithm shines when paired with a custom-designed analog projector, which enables 1-megapixel high-speed 3D scanning at up to 500 fps. We describe our algorithm and hardware prototype for high-speed 3D scanning and compare them with commercial and open-source structured light scanning methods.