Depth Super-Resolution by Texture-Depth Transformer
Chao Yao, Shuaiyong Zhang, Mengyao Yang, Meiqin Liu, Junpeng Qi · 2021
Depth maps have been still suffering from some non-negligible effects, resulting from the consumer-level sensors. The limited resolution of the acquired depth maps is one of these annoying issues. Many prominent researchers have recently made a lot of efforts, such as traditional filters, as well as the deep learning paradigms. However, depth super-resolution is still an open challenge. In this paper, we design a texture-depth transformer for depth super-resolution task, which can learn the corresponding structural information of the high-resolution texture images and the corresponding interpolated depth maps. Moreover, a multi-scale feature fusion strategy is exploited to further enhance the fusion feature. Complementary to a quantitative evaluation, we demonstrate the effectiveness of the proposed approach.