Image-based reconstruction of specular surfaces usually requires dense correspondences between image features and points in the environment. In natural environments, these points are usually unknown and correspondences often exist only sparsely between pairs of images. These assumptions complicate the reconstruction problem by introducing many ambiguities which can often only be resolved using regularization of the surface. Only very recently, work has been presented which is able to reconstruct specular surfaces using different kinds of algorithms.

This thesis gives an introduction to the different types of ambiguities and presents a framework which tries to resolve these through regularization using a multi-view approach in combination with a low-parametric surface. The reconstruction method is modeled as an iterative optimization in order to achieve specular consistency. This consistency is based on the laws of reflection applied to the viewing rays which are given by image-to-image features. The framework is capable of processing different kinds of additional input data, e.g. known environmental features or boundary points on the surface.

Synthetic and real-world experiments were executed using both known and unknown feature positions. Results on synthetic datasets show accurate reconstructions even in the presence of specular consistent ambiguities. An adapted outlier removal for feature matching on image series of specular objects was applied to real-wold input data. The results show that it is possible to reconstruct the surface of mirroring objects even with sparse input data.