This paper tackles the task of uncalibrated photometric stereo for 3D object
reconstruction, where both the object shape, object reflectance, and lighting
directions are unknown. This is an extremely difficult task, and the challenge
is further compounded with the existence of the well-known generalized
bas-relief (GBR) ambiguity in photometric stereo. Previous methods to resolve
this ambiguity either rely on an overly simplified reflectance model, or assume
special light distribution. We propose a new method that jointly optimizes
object shape, light directions, and light intensities, all under general
surfaces and lights assumptions. The specularities are used explicitly to solve
uncalibrated photometric stereo via a neural inverse rendering process. We
gradually fit specularities from shiny to rough using novel progressive
specular bases. Our method leverages a physically based rendering equation by
minimizing the reconstruction error on a per-object-basis. Our method
demonstrates state-of-the-art accuracy in light estimation and shape recovery
on real-world datasets.

本文旨在解决在未知物体形状、物体反射率和光照方向的情况下进行三维物体重建的任务，提出了一种新的利用神经反演渲染和渐进式反射率基础来解决未标定光度测量问题的方法。该方法在真实世界数据集上表现出最先进的精度。