This study explores the use of photometric techniques (shape-from-shading and uncalibrated photometric stereo) for upsampling the low-resolution depth map from an RGB-D sensor to the higher resolution of the companion RGB image. A single-shot variational approach is first put forward, which is effective as long as the target's reflectance is piecewise-constant. It is then shown that this dependency upon a specific reflectance model can be relaxed by focusing on a specific class of objects (e.g., faces), and delegate reflectance estimation to a deep neural network. A multi-shots strategy based on randomly varying lighting conditions is eventually discussed. It requires no training or prior on the reflectance, yet this comes at the price of a dedicated acquisition setup. Both quantitative and qualitative evaluations illustrate the effectiveness of the proposed methods on synthetic and real-world scenarios.

本研究探索了使用光度学技术（阴影形状和非标定光度立体）将RGB-D传感器的低分辨率深度图上采样到伴随的RGB图像的高分辨率。首先提出了一种有效的单次变分方法，而这依赖于目标的反射率是分段常数。随后，展示了这个依赖特定反射模型的限制可以通过关注特定类别的对象（例如面部），并将反射率估计委托给深度神经网络来缓解。最后讨论了一种基于随机变化光照条件的多次采集策略。这不需要反射率的培训或先前知识，但其代价是专用采集设置。通过定量和定性评估，证明了所提出方法在合成和真实场景中的有效性。

光度深度超分辨率