Wöhler, ChristianLenoch, Malte2019-04-082019-04-082019http://hdl.handle.net/2003/38005http://dx.doi.org/10.17877/DE290R-19988The camera-based acquisition of the environment has become an ordinary task in today’s society as much in science as in everyday-life situations. Smartphone cameras are employed in interactive video games and augmented reality, just as industrial quality inspection, remote sensing, robotics and autonomous vehicles rely on camera sensors to analyze the outside world. One crucial aspect of the automated analysis is the retrieval of the 3D structure of unknown objects in the scene – be it for collision prevention, grabbing, or comparison to a CAD model – from the acquired image data. Reflectance-based surface reconstruction methods form a valuable part of the set of camera-based algorithms. Stereo cameras exploit geometrical optics to triangulate the 3D position of a scene point while photometric procedures require one camera only and estimate a surface gradient field based on the shading of an object. The reflectance properties of the object have to be known to achieve this which results in a chicken-and-egg problem on unknown objects since the surface shape has to be available to approximate the reflectance properties, and the reflectance properties have to be known to estimate the surface shape. This situation is circumvented on Lambertian surfaces, yet, those that are of interest in real-world applications exhibit much more complex reflectance properties for which this problem remains. The challenge of estimating the unknown spatially varying bidirectional reflectance distribution function (BRDF) parameters of an object of approximately known shape is approached from a Bayesian perspective employing reversible jump Markov chain Monte Carlo methods to infer both, reflectance parameters and surface regions that show similar reflectance properties from sampling the posterior distributions of the data. A significant advantage compared to non-linear least squares estimates is the availability of statistical information that can directly be used to evaluate the accuracy of the inferred patches and parameters. In the evaluation of the method, the derived patches accurately separate a synthetic and a laboratory dataset into meaningful segments. The reflectance of the synthetic dataset is almost perfectly reproduced and misestimated BRDF parameters underline the necessity for a large dataset to apply statistical inference. The real-world dataset reveals the inherent problems of BRDF estimation in the presence of cast shadows and interreflections. Furthermore, a procedure that is suitable to calibrate a two-camera photometric stereo acquisition setup is examined. The calibration is based on multiple images of a diffuse spherical object that is located in corresponding images. Although the calibration object is supposed to be perfectly diffuse by design, considering a specular Phong component in addition to the Lambertian BRDF model increases the accuracy of the rendered images. The light source positions are initialized based on stereo geometry and optimized by minimizing the intensity error between measured and rendered images of the calibration object. Ultimately, this dissertation tackles the task of image-based surface reconstruction with the contribution of two novel algorithms. The first one computes an initial approximation of the 3D shape based on the diffuse component of the reflectance and iteratively refines this rough guess with gradient fields calculated from photometric stereo assuming a combination of the BRDF models of Lambert and Blinn. The second method computes the surface gradient fields for both views of a stereo camera setup and updates the estimated depth subject to Horn’s integrability constraint and a new regularization term that accounts for the disparity offset between the two matching gradient fields. Both procedures are evaluated on objects that exhibit complex reflectance properties and challenging shapes. A fringe projection 3D scanner is used for reference data and error assessment. Small details that are not visible in the coarse initial 3D data, that is supplied to the first algorithm, are recovered based on the high-quality gradient data obtained from photometric stereo. The error of the test data with respect to the reference scanner is less than 0.3 mm. In contrast to the first method that computes shape information, the stereo camera algorithm yields absolute 3D data and produces very good reconstruction results on all datasets. The proposed method even surpasses the reconstruction accuracy of the 3D scanner on a metallic dataset. This is a notable contribution, as most existing camera-based surface reconstruction methods exclusively handle diffusely reflecting objects and those that focus on non-Lambertian objects still struggle with highly specular metallic surfaces.en3D surfache reconstructionMulti-view photometric stereoShape from shadingReflectance modelingBayesian parameter estimation620TextBayesianisches VerfahrenShading