In this paper, we introduce SDM-UniPS, a groundbreaking Scalable, Detailed,
Mask-free, and Universal Photometric Stereo network. Our approach can recover
astonishingly intricate surface normal maps, rivaling the quality of 3D
scanners, even when images are captured under unknown, spatially-varying
lighting conditions in uncontrolled environments. We have extended previous
universal photometric stereo networks to extract spatial-light features,
utilizing all available information in high-resolution input images and
accounting for non-local interactions among surface points. Moreover, we
present a new synthetic training dataset that encompasses a diverse range of
shapes, materials, and illumination scenarios found in real-world scenes.
Through extensive evaluation, we demonstrate that our method not only surpasses
calibrated, lighting-specific techniques on public benchmarks, but also excels
with a significantly smaller number of input images even without object masks.

本文介绍了 SDM-UniPS，一种具有突破性的可扩展，详细，无遮挡和通用的光度立体网络。我们的方法可以恢复惊人复杂的表面法线图，即使在未知的，空间变化的采集条件和非受控环境下。我们已经扩展了先前的通用光度立体网络，以提取空间光特性，利用高分辨率输入图像中的所有可用信息，并考虑表面点之间的非局部交互。此外，我们提出了一个新的合成训练数据集，包括现实世界场景中的各种形状，材料和照明情况。通过广泛的评估，我们证明了我们的方法不仅可以胜过公共基准上的校准的，特定于照明的技术，而且即使没有目标掩模，也可以在显著较少的输入图像下表现出色。