Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline
This work addresses a practical issue for computer vision applications by providing a dataset and method to improve depth map quality, though it is incremental as it builds on existing super-resolution techniques.
The paper tackles the problem of low-resolution depth maps from commercial sensors by constructing a real-world dataset of paired low- and high-resolution depth maps and proposing a fast super-resolution baseline method, achieving more accurate results with clearer boundaries and correcting depth errors in real-world scenarios.
Depth maps obtained by commercial depth sensors are always in low-resolution, making it difficult to be used in various computer vision tasks. Thus, depth map super-resolution (SR) is a practical and valuable task, which upscales the depth map into high-resolution (HR) space. However, limited by the lack of real-world paired low-resolution (LR) and HR depth maps, most existing methods use downsampling to obtain paired training samples. To this end, we first construct a large-scale dataset named "RGB-D-D", which can greatly promote the study of depth map SR and even more depth-related real-world tasks. The "D-D" in our dataset represents the paired LR and HR depth maps captured from mobile phone and Lucid Helios respectively ranging from indoor scenes to challenging outdoor scenes. Besides, we provide a fast depth map super-resolution (FDSR) baseline, in which the high-frequency component adaptively decomposed from RGB image to guide the depth map SR. Extensive experiments on existing public datasets demonstrate the effectiveness and efficiency of our network compared with the state-of-the-art methods. Moreover, for the real-world LR depth maps, our algorithm can produce more accurate HR depth maps with clearer boundaries and to some extent correct the depth value errors.