Deep Semi-calibrated Near-field Photometric Stereo

Abstract

Light calibration plays a crucial role in near-field photometric stereo for accurate surface normal estimation. It comprises light position calibration and light intensity calibration. In industrial applications, the light position calibration is typically performed once, as the facility remains fixed. However, the light intensity calibration needs to be repeated for real industrial scenes with variations in light intensity and fails for autoexposure industrial scenes. This study introduces a deep semi-calibrated near-field photometric stereo technique that achieves precise surface normal estimation without light intensity calibration. The technique consists of two parts: the near-field light intensity calibration network (NFLICN) and the near-field normal estimation network (NFNEN). NFLICN utilizes a point light model to self-calibrate pixel-wise light intensity. NFNEN, on the other hand, takes the calibrated light intensity as input and effectively utilizes local image features to estimate the surface normal. Experimental results on both synthetic testing datasets and the benchmark LUCES dataset confirm the state-of-the-art accuracy of our proposed method in surface normal estimation, even without light intensity calibration.