Evaluating the Robustness of Depth Image Super-Resolution Models

Depth image super-resolution (DISR) is one of the hot topics in computer vision. Although great progress has been made in this research topic, the robustness of DISR models is not sufficiently investigated, which is of great importance in the real applications. Accordingly, in this paper, we make an initial attempt to investigate the robustness of DISR models. Specifically, we test their generalization ability when the input depth image suffers from visual quality degradation. To facilitate this study, we construct a large-scale depth image dataset in which the reference depth images are perturbed to generate the degraded depth images automatically. Then, we test six top-performing DISR models on the constructed dataset and then compare their strengths and weaknesses. By conducting comprehensive experiments, we find that depth image super-resolution models perform poorly on Gaussian noise, and that the higher the level, the lower the quality of the predicted depth map. Furthermore, some DISR models only outperform at lower magnifications (such as 2x and 4x).