High Quality Surface Reconstruction Using Photometric Stereo

Three-dimensional surface reconstruction is an important branch of Computer Vision. It’s widely used in the quality control of products, industrial measurement, medical diagnosis, digital preservation of relic, scene evidence, digital entertainment and virtual reality. In some application areas, local features of measured object can not be well reconstructed based on the geometric measurement methods which makes the details of reconstructed surface quality greatly degraded, and often can not meet the requirements. On the other hand, Photometric Stereo scheme can keep local details of measured objects well. It’s quite satisfactory for Computer Vision applications and greatly complement geometric measurement based methods. Thus Photometric Stereo has received wide attention in the research field.Since Woodham first proposed the concept and realization method of Photometric Stereo, its huge potential in the three-dimensional reconstruction of scene details has been noticed. In the past thirty years, scholars have done many meaningful research and exploration in this area and it is still a hot topic and trend now. After analyzing the recent research approach in Photometric Stereo field, we have focused on the light sources calibration, non-Lambertian surface normal estimation and shape from gradient fields. By keeping highly precision in each step of Photometric Stereo using proposed methods, the final reconstruction outperform state-of-the-art techniques. The major work and contributions lie in several fields as follows:(1) Using the parameters acquired by camera calibration and the geometric&photometric in-formation from the planar surface, we propose a novel method for light sources calibration. Differ-ing from the traditional sphere based methods, proposed method just uses a plane mirror which is divided into mirror reflection area and diffuse area respectively. The accuracy of proposed method is improved by an order of magnitude compared to the traditional methods. So it can guarantee the precision of normal estimation in Photometric Stereo.(2) Based on co-linear light source configuration, we propose a non-Lambertian surface nor-mal estimation method. Using deviations in photometric images under the co-linear light sources, the specularity detection problem is converted to pattern classification one. In training step we use real or synthetical data to build a robust specularity classifier. After discarding specularity in testing step, the residual error in photometric images is sparsely distributed. An l1-norm approximation method is designed to further correct the sparse error. The final output is quite satisfactory.(3) We convert the traditional Poisson equation to its kernel representation form using kernel method, the underlying surface can be recovered from Gaussian noise contaminated gradient data by kernel regression. With further considering an even extension of the original gradient field, we assume a Neumann boundary condition which makes final reconstructed surface smooth and reliable.(4) Inspired by the idea of sparse expression in compressive sensing fields, we propose andecoding method for surface reconstruction from gradient fields. The l1decoding procedure can exactly recover surface from sparse noise contaminated gradient data. The Laplacian term is additionally employed to increase the information in decoding matrix and suppress noise and/or outliers. Experimental results validate that the proposed method significantly outperforms state-of-the-art techniques, and can produce satisfactory reconstruction even in the very extreme situation of60%outliers.