  This paper aims at recovering the shape of a scene with unknown,
non-Lambertian, and possibly spatially-varying surface materials. When the
shape of the object is highly complex and that shadows cast on the surface, the
task becomes very challenging. To overcome these challenges, we propose a
coordinate-based deep MLP (multilayer perceptron) to parameterize both the
unknown 3D shape and the unknown reflectance at every surface point. This
network is able to leverage the observed photometric variance and shadows on
the surface, and recover both surface shape and general non-Lambertian
reflectance. We explicitly predict cast shadows, mitigating possible artifacts
on these shadowing regions, leading to higher estimation accuracy. Our
framework is entirely self-supervised, in the sense that it requires neither
ground truth shape nor BRDF. Tests on real-world images demonstrate that our
method outperform existing methods by a significant margin. Thanks to the small
size of the MLP-net, our method is an order of magnitude faster than previous
CNN-based methods.

Computer Vision and Pattern Recognition