Depth Map Fusion with Camera Position Refinement

Abstract We present a novel algorithm for image-based surface reconstruction from a set of calibrated images. The problem is formulated in Bayesian framework, where esti-mates of depth and visibility in a set of selected cameras are iteratively improved. The core of the algorithm is the min-imisation of overall geometric L2 error between measured 3D points and the depth estimates. In the visibility estimation task, the algorithm aims at out-lier detection and noise suppression, as both types of errors are often present in the stereo output. The geometrical for-mulation allows for simultaneous refinement of the external camera parameters, which is an essential step for obtaining accurate results even when the calibration is not precisely known. We show that the results obtained with our method are comparable to other state-of-the-art techniques.