Handheld structured light system for panoramic 3D measurement in mesoscale

Abstract

The measurement of complete 3D topography in mesoscale plays a vital role in high-precision reverse engineering, oral medical modeling, circuit detection, etc. Traditional structured light systems are limited to measuring 3D shapes from a single perspective. How to achieve high-quality mesoscopic panoramic 3D measurement remains challenging, especially in complex measured scenarios such as dynamic measurement, scattering medium, and high reflectance. To overcome these problems, we develop a handheld mesoscopic panoramic 3D measurement system for such complex scenes together with the fast point-cloud-registration and accurate 3D-reconstruction, where a motion discrimination mechanism is designed to ensure that the captured fringe is in a quasi-stationary case by avoiding the motion errors caused during fringe scanning; a deep neural network is utilized to suppressing the fringe-degradation caused by scattering mediums resulting to significantly improves the quality of the 3D point cloud; a strategy based on phase averaging is additionally proposed to simultaneously correct the saturation-induced errors and gamma nonlinear errors. Finally, the proposed system with a multi-threaded data processing framework is further developed to verify the proposed method and the corresponding experiments verify its feasibility.