A Lightweight, Real-Time, and High-Precision Calibrated Photometric Stereo Measurement Method

Photometric stereo represents a sophisticated technique utilized to capture high-fidelity surface textures and morphologies under varying illumination angles, with substantial applications in fields such as quality assurance and cultural heritage preservation. However, contemporary implementations often grapple with challenges in achieving high-precision measurements that are both economically feasible and conducive to real-time processing. To address these challenges, we propose a Dual-Scale Feature Fusion (DSFF) photometric stereo network coupled with a Shadow-Fusing Supervised (SFS) training strategy. By aggregating dual-scale features and effectively leveraging shadow-occluded regions, the proposed approach overcomes limitations of existing techniques, enabling low-cost, real-time, and high-precision measurement of complex morphologies across diverse materials. Experimental results on four authoritative public datasets demonstrate that our methods outperform nearly 30 state-of-the-art approaches in key metrics such as measurement accuracy, memory consumption, and inference time. These results highlight the breakthrough improvements and the immense potential for application in various fields. The code used in this study is publicly accessible at https://github.com/zz-ux/PS-Measurement.