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We propose an approach for dense semantic 3D reconstruction which uses a dataterm that is defined as potentials over viewing rays, combined with continuoussurface area penalization. Our formulation is a convex relaxation which weaugment with a crucial non-convex constraint that ensures exact handling ofvisibility. To tackle the non-convex minimization problem, we propose amajorize-minimize type strategy which converges to a critical point. Wedemonstrate the benefits of using the non-convex constraint experimentally. Forthe geometry-only case, we set a new state of the art on two datasets of thecommonly used Middlebury multi-view stereo benchmark. Moreover, ourgeneral-purpose formulation directly reconstructs thin objects, which areusually treated with specialized algorithms. A qualitative evaluation on thedense semantic 3D reconstruction task shows that we improve significantly overprevious methods.

Semantic 3D Reconstruction with Continuous Regularization and Ray Potentials Using a Visibility Consistency Constraint