This paper tackles a new photometric stereo task, named universal photometric
stereo. Unlike existing tasks that assumed specific physical lighting models;
hence, drastically limited their usability, a solution algorithm of this task
is supposed to work for objects with diverse shapes and materials under
arbitrary lighting variations without assuming any specific models. To solve
this extremely challenging task, we present a purely data-driven method, which
eliminates the prior assumption of lighting by replacing the recovery of
physical lighting parameters with the extraction of the generic lighting
representation, named global lighting contexts. We use them like lighting
parameters in a calibrated photometric stereo network to recover surface normal
vectors pixelwisely. To adapt our network to a wide variety of shapes,
materials and lightings, it is trained on a new synthetic dataset which
simulates the appearance of objects in the wild. Our method is compared with
other state-of-the-art uncalibrated photometric stereo methods on our test data
to demonstrate the significance of our method.

本文介绍了一种名为 “通用光度立体” 的新光度立体任务，旨在开发出一种无需假定特定光照模型即可适用于各种形状、材料和光照变化的解决方案算法，该算法的实现基于一种纯数据驱动的方法，通过提取通用的照明表示 —— 全局照明环境，代替了以往的物理光照参数恢复，同时通过使用设计的合成数据集训练神经网络，以根据各种形状、材料和光照适应一组新的数据集。通过与其他最先进的未校准光度立体方法进行比较，以演示我们方法的显著性。