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Recent advances in 3D reconstruction allows to acquire highly detailed geometry from a set of images. The outcome of vision-based reconstruction methods is often oversampled, noisy, and without any higher-level information. Further processing such as object recognition, physical measurement, urban modeling, or rendering requires more advanced representations such as computer-aided design (CAD) models. In this paper, we present a global approach that simultaneously decomposes a triangulated surface into meaningful segments and fits a set of bounded geometric primitives. Using the theory of Markov chain Monte Carlo methods (MCMC), a random process is derived to find the solution that most likely explains the measured data. A data-driven approach based on the random sample consensus (RANSAC) paradigm is designed to guide the optimization process with respect to efficiency and robustness. It is shown, that graph cuts allow to incorporate model complexity and spatial regularization into the MCMC process. The algorithm has successfully been implemented and tested on various examples.

Semantic Segmentation and Model Fitting for Triangulated Surfaces using Random Jumps and Graph Cuts