  Estimating the pose of an object from a monocular image is an inverse problem
fundamental in computer vision. The ill-posed nature of this problem requires
incorporating deformation priors to solve it. In practice, many materials do
not perceptibly shrink or extend when manipulated, constituting a powerful and
well-known prior. Mathematically, this translates to the preservation of the
Riemannian metric. Neural networks offer the perfect playground to solve the
surface reconstruction problem as they can approximate surfaces with arbitrary
precision and allow the computation of differential geometry quantities. This
paper presents an approach to inferring continuous deformable surfaces from a
sequence of images, which is benchmarked against several techniques and obtains
state-of-the-art performance without the need for offline training.
