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.

翻译：本文提出了SDM-UniPS，一种突破性的可扩展、高细节、无遮罩通用光度立体视觉网络。我们的方法能够在非受控环境下未知、空间变化光照条件下拍摄的图像中，恢复出媲美3D扫描仪的精妙表面法向图。我们将先前的通用光度立体视觉网络扩展为可提取空间光照特征，充分利用高分辨率输入图像中的所有信息，并考虑表面点之间的非局部相互作用。此外，我们构建了一个新的合成训练数据集，涵盖现实世界场景中各种形状、材质和光照场景。通过广泛评估，我们证明该方法不仅在公开基准上超越了标定式特定光照技术，而且即使无需物体遮罩，也能显著减少输入图像数量并取得优异性能。