Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensity at each pixel. Photometric stereo prevails in superior per-pixel resolution and fine reconstruction details. However, it is a complicated problem because of the non-linear relationship caused by non-Lambertian surface reflectance. Recently, various deep learning methods have shown a powerful ability in the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo methods. We first analyze these methods from different perspectives, including input processing, supervision, and network architecture. We summarize the performance of deep learning photometric stereo models on the most widely-used benchmark data set. This demonstrates the advanced performance of deep learning-based photometric stereo methods. Finally, we give suggestions and propose future research trends based on the limitations of existing models.
翻译:光度立体器从多个图像中恢复物体的表面常态,有不同的阴影提示,即对地表方向和每个像素强度之间的关系进行建模。光度立体器在高级像素分辨率和精细重建细节中占上风。然而,由于非蓝贝特表面反射造成的非线性关系,这是一个复杂的问题。最近,各种深层学习方法在针对非蓝贝特表面的光度立体的光度立体方面表现出了强大的能力。本文全面审查了现有的深层学习校准光度立体法。我们首先从不同的角度分析这些方法,包括输入处理、监督和网络结构。我们总结了最广泛使用的基准数据集中深层学习光度立体模型的性能。这显示了深层学习光度立体法的先进性能。最后,我们根据现有模型的局限性提出建议并提出未来研究趋势。