Super-resolution is one of the most classical inverse problems in computer vision. Given one low-resolution and possibly noisy input image, it aims at estimating a higher-resolution and denoised image. Efficiently solving this problem is crucial in low-cost 3D-sensing, since consumer depth sensors such as Microsoft's Kinect provide a very low resolution depth image which is prone to strong quantization and noise artifacts. On the other hand, such sensors usually also provide a companion RGB image which is typically of higher resolution and better quality. Single-view 3D-reconstruction could be achieved using solely these color clues, however this task is another ill-posed inverse problem, known as shape-from-shading. In this talk, I will show how these two ill-posed problems can be jointly solved within a variational framework, by using the low-resolution depth clues to disambiguate shape-from-shading or, symmetrically, using the high-resolution shading clues to disambiguate depth super-resolution. The numerical solving of the resulting non-convex variational problem using an augmented Lagrangian approach will be discussed, and real-world experimental results will be presented.