Improving speckle projection profilometry with deep learning-based digital image correlation

Abstract Existing speckle pattern profilometry (SPP) methods for 3D shape measurement generally adopt subset/template-based image matching algorithms for disparity estimation. Despite their attractive merit in single-shot 3D reconstruction, these methods are computationally intensive and often suffer from data loss in regions with sharp gray-level variations or complex surface geometries. To overcome these limitations, an improved SPP using a novel deep-learning based pixelwise image registration framework is introduced. Specifically, a SPP system utilizing a calibrated stereovision setup integrated with a digital projector is established to capture projected speckle patterns. Disparity between the stereo pair that encodes the surface topography is estimated using a state-of-the-art deep learning-based digital image correlation (DL-DIC) framework, called G-RAFT. The estimated disparity is subsequently combined with stereo calibration parameters for triangulation-based 3D reconstruction. By using G-RAFT, the proposed DL-DIC-aided SPP achieves efficient, single-shot, high-accuracy, and dense 3D shape measurement. Experimental results on standard geometries demonstrate that the proposed method achieves reconstruction accuracy comparable to traditional subset/template-based algorithm (Smart-DIC) while providing higher computational efficiency. Experiments on a complex plaster bust further show that this DL-DIC-aided SPP improves reconstruction completeness and robustness in regions where traditional subset-based matching exhibits data loss or mismatches. These results demonstrate that the proposed DL-DIC-aided SPP enables accurate, robust, and dense single-shot 3D reconstruction while preserving the explicit calibration-based geometric reconstruction pipeline.