LAC-PS: A Light Direction Selection Policy Under the Accuracy Constraint for Photometric Stereo

Photometric stereo (PS) methods recover surface normals from appearance changes under varying light directions, excelling in tasks like 3D surface reconstruction and defect inspection. However, collecting the illumination images is expensive, and current PS methods cannot obtain the light direction set that satisfies the pre-defined accuracy constraint, limiting their adaptability to various applications with varying accuracy requirements. To address this issue, we propose the LAC-PS, a light direction selection policy under the accuracy constraint for photometric stereo, which optimizes the light direction set to meet target reconstruction accuracy. In our method, we develop an accuracy assessment network that estimates reconstruction accuracy without ground truth. With this estimated accuracy, we put forward a reinforcement learning-based method that can utilize policy to sequentially select light directions and obtain the light directions satisfying the desired PS recovery accuracy constraint. Experimental results on real and synthetic datasets demonstrate that our method effectively selects light directions that satisfy accuracy constraints.