Our work was inspired by recent advances in image segmentation where flux based functionals significantly improved alignment of object boundaries. We propose a novel "photoflux" functional for multi-view 3D reconstruction that is closely related to properties of photohulls. Our photohull prior can be combined with regularization. Thus, this work unifies two major groups of multiview stereo techniques: “space carving” and “deformable models”. Our approach combines benefits of both groups and allows to recover fine shape details without oversmoothing while robustly handling noise. Photoflux provides data-driven ballooning force helping to segment thin structures or holes. We propose a number of different versions of photoflux based on global, local, or non-deterministic visibility models. Some forms of photoflux can be easily added into standard regularization techniques. For other forms we propose new optimization methods. We also show that photoflux maximizing shapes can be seen as regularized Laplacian zero-crossings.