We propose a method for predicting the 3D shape of a deformable surface from a single view. By contrast with previous approaches, we do not need a pre-registered template of the surface, and our method is robust to the lack of texture and partial occlusions. At the core of our approach is a {it geometry-aware} deep architecture that tackles the problem as usually done in analytic solutions: first perform 2D detection of the mesh and then estimate a 3D shape that is geometrically consistent with the image. We train this architecture in an end-to-end manner using a large dataset of synthetic renderings of shapes under different levels of deformation, material properties, textures and lighting conditions. We evaluate our approach on a test split of this dataset and available real benchmarks, consistently improving state-of-the-art solutions with a significantly lower computational time.


computer vision, optimisation.

Author keywords

3D Reconstruction

Scientific reference

A. Pumarola, A. Agudo, L. Porzi, A. Sanfeliu, V. Lepetit and F. Moreno-Noguer. Geometry-aware network for non-rigid shape prediction from a single view, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, Salt Lake City, UT, USA, pp. 4681-4690.