Abstract

We propose a convex framework for silhouette and stereo fusion in 3D reconstruction from multiple images. The key idea is to show that the reconstruction problem can be cast as one of minimizing a convex functional where the exact silhouette consistency is imposed as a convex constraint that restricts the domain of admissible functions. As a consequence, we can retain the original stereo-weighted surface area as a cost functional without heuristic modifications by balloon terms or other strategies, yet still obtain meaningful (nonempty) global minimiz- ers. Compared to previous methods, the introduced approach does not depend on initialization and leads to a more robust numerical scheme by removing the bias near the visual hull boundary. We propose an ef- ficient parallel implementation of this convex optimization problem on a graphics card. Based on a photoconsistency map and a set of im- age silhouettes we are therefore able to compute highly-accurate and silhouette-consistent reconstructions for challenging real-world data sets in less than one minute.