  The goal of photometric stereo is to measure the precise surface normal of a
3D object from observations with various shading cues. However, non-Lambertian
surfaces influence the measurement accuracy due to irregular shading cues.
Despite deep neural networks have been employed to simulate the performance of
non-Lambertian surfaces, the error in specularities, shadows, and crinkle
regions is hard to be reduced. In order to address this challenge, we here
propose a photometric stereo network that incorporates Lambertian priors to
better measure the surface normal. In this paper, we use the initial normal
under the Lambertian assumption as the prior information to refine the normal
measurement, instead of solely applying the observed shading cues to deriving
the surface normal. Our method utilizes the Lambertian information to
reparameterize the network weights and the powerful fitting ability of deep
neural networks to correct these errors caused by general reflectance
properties. Our explorations include: the Lambertian priors (1) reduce the
learning hypothesis space, making our method learn the mapping in the same
surface normal space and improving the accuracy of learning, and (2) provides
the differential features learning, improving the surfaces reconstruction of
details. Extensive experiments verify the effectiveness of the proposed
Lambertian prior photometric stereo network in accurate surface normal
measurement, on the challenging benchmark dataset.
