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We propose a convex formulation 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 convex constraints that restrict the domain of feasible functions. As a consequence, we can retain the original stereo-weighted surface...
In this work we propose a convex relaxation approach for computing minimal partitions. Our approach is based on rewriting the minimal partition problem (also known as Potts model) in terms of a primal dual Total Variation functional. We show that the Potts prior can be incorporated by means of convex constraints on the dual variables. For minimization we propose an efficient primal dual projected...
In this paper, we propose a general technique for robust modeling of non-planar ground surfaces. It is based on representing the ground plane as a parametric B-Spline surface and on robust tracking of the surface parameters using stereo disparities and a Kalman filter. We adopt a recently published real-time optimization algorithm for finding the road-obstacle boundary to integrate disparity values...
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