On the Regularising Levenberg-Marquardt Method for Blinn-Phong Photometric Stereo
This work addresses the challenge of accurately reconstructing 3D shapes from images with specular effects for computer vision applications, representing an incremental improvement over Lambertian-based methods.
The paper tackles the non-linear optimization problem in photometric stereo when using the Blinn-Phong reflectance model to handle specular highlights, and it develops a regularizing Levenberg-Marquardt method with an explicit bound for convergence reliability and numerical correctness verification.
Photometric stereo refers to the process to compute the 3D shape of an object using information on illumination and reflectance from several input images from the same point of view. The most often used reflectance model is the Lambertian reflectance, however this does not include specular highlights in input images. In this paper we consider the arising non-linear optimisation problem when employing Blinn-Phong reflectance for modeling specular effects. To this end we focus on the regularising Levenberg-Marquardt scheme. We show how to derive an explicit bound that gives information on the convergence reliability of the method depending on given data, and we show how to gain experimental evidence of numerical correctness of the iteration by making use of the Scherzer condition. The theoretical investigations that are at the heart of this paper are supplemented by some tests with real-world imagery.