Task-Specific Near-Field Photometric Stereo for Measuring Metal Surface Texture

Surface texture measurement helps control the quality of large workpieces produced by machine systems. Current optical measurement methods, e.g., fringe projection profilometry and coherent scanning interferometry, are difficult to perform full-field measurements of microtextures on large-sized surfaces. Photometric stereo could potentially address this challenge; however, machined metal surfaces exhibit highly reflective non-Lambertian reflectance that dramatically decreases its effectiveness. To solve this problem, a task-specific near-field photometric stereo approach is proposed to dramatically enhance the accuracy of the surface normal estimation on machined metal surfaces. First, a near-field photometric stereo network is designed for efficient industrial applications, which fully employs the pixelwise information under a small number of lights to achieve surface normal estimation. Then, a task-specific training strategy is proposed to train the proposed network, where a task-specific real dataset is established for each specific combination of material and machine processing to optimize the network parameters initially trained by a synthetic dataset. Experiments on synthetic sinusoidal surfaces and real machined surfaces validated the superiority of the proposed method for metal reflectance compared with the state-of-the-art photometric stereo methods and the sub/micrometer-level sensitivity to surface height variations. Two case studies on tool marks on a large freeform surface and defects on a stamping surface are presented, demonstrating their potential for industrial applications.