Anisotropic Neural Reflectance Model for Measuring Curved Machined Metal Surface

The highlight reflectance on machined metal surfaces dramatically influences the performance of optical measurement sensors, such as fringe projector profilometers. Previous research addressed such an issue through fusion of images under different camera exposure times or modulation of the lighting intensity of the projector. Differently, this article provides a new research perspective that resorts to the modeling of complicated anisotropic reflectance on machined metal surfaces, which essentially causes the decrease in the quality of fringe images. In this article, an anisotropic neural reflectance model is proposed for the effective description of machined metal reflectance. Then, a facility with one camera, one projector, and several lighting emittance didoes is designed for the application of the proposed model in the measurement of curved machined metal surfaces. A complete and accurate point cloud is finally obtained through fitting of the real reflectance using the proposed model in a self-supervision way. Synthetic experiments illustrate that the capability of the proposed model to describe the machined metal reflectance is considerably enhanced compared with the traditional parameterized reflectance model and isotropic neural reflectance model. Real experiments prove that the proposed method can achieve measurement accuracy of 23 μm on machined metal surfaces with repeated accuracy of 0.15 μm, where the ground truth is given by the coordinate measuring machine.