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Performing super-resolution of a depth image using the guidance from an RGBimage is a problem that concerns several fields, such as robotics, medicalimaging, and remote sensing. While deep learning methods have achieved goodresults in this problem, recent work highlighted the value of combining modernmethods with more formal frameworks. In this work, we propose a novel approachwhich combines guided anisotropic diffusion with a deep convolutional networkand advances the state of the art for guided depth super-resolution. The edgetransferring/enhancing properties of the diffusion are boosted by thecontextual reasoning capabilities of modern networks, and a strict adjustmentstep guarantees perfect adherence to the source image. We achieve unprecedentedresults in three commonly used benchmarks for guided depth super-resolution.The performance gain compared to other methods is the largest at larger scales,such as x32 scaling. Code(https://github.com/prs-eth/Diffusion-Super-Resolution) for the proposed methodis available to promote reproducibility of our results.

Guided Depth Super-Resolution by Deep Anisotropic Diffusion