A numerical solution to shape-from-shading under natural illumination is presented. It builds upon an augmented Lagrangian approach for solving a generic PDE-based shape-from-shading model which handles directional or spherical harmonic lighting, orthographic or perspective projection, and greylevel or multi-channel images. Real-world applications to shading-aware depth map denoising, refinement and completion are presented.

Figure 1: A 3D reconstruction pipeline in Meshroom. The pipeline is shown at the bottom left, the input images on the top left, the output of the highlighted pipeline node (in this case the Structure-from-Motion node along with the camera poses) are shown on the top right, info about this node at the bottom right.

We tackle the problem of reflectance estimation from a set of multi-view images, assuming known geometry. The approach we put forward turns the input images into reflectance maps, through a robust variational method. The variational model comprises an image-driven fidelity term and a term which enforces consistency of the reflectance estimates with respect to each view. If illumination is fixed across the views, then reflectance estimation remains under-constrained: a regularization term, which ensures piecewise-smoothness of the reflectance, is thus used. Reflectance is parameterized in the image domain, rather than on the surface, which makes the numerical solution much easier, by resorting to an alternating majorization-minimization approach. Experiments on both synthetic and real-world datasets are carried out to validate the proposed strategy.

We put forward a simple, yet effective splitting strategy for multi-view stereopsis. It recasts the minimization of the classic photoconsistency + gradient regularization functional as a sequence of simple problems which can be solved efficiently. This framework is able to handle various photo-consistency measures and regularization terms, and can be used for instance to estimate either a minimal-surface or a shading-aware solution. The latter makes the proposed approach very effective for dealing with the well-known problem of textureless objects 3D-reconstruction.

OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Abstract. We introduce a variational framework for separating shading and reflectance from a series of images acquired under different angles, when the geometry has already been estimated by multi-view stereo.Our formulation uses an l 1 -TV variational framework, where a robust photometric-based data term enforces adequation to the images, total variation ensures piecewise-smoothness of the reflectance, and an additional multi-view consistency term is introduced for resolving the arising ambiguities. Optimisation is carried out using an alternating optimisation strategy building upon iteratively reweighted least-squares. Preliminary results on both a synthetic dataset, using various lighting and reflectance scenarios, and a real dataset, confirm the potential of the proposed approach.