Real depth super-resolution (DSR), unlike synthetic settings, is a challenging task due to the structural distortion and the edge noise caused by the natural degradation in real-world low-resolution (LR) depth maps. These defeats result in significant structure inconsistency between the depth map and the RGB guidance, which potentially confuses the RGB-structure guidance and thereby degrades the DSR quality. In this paper, we propose a novel structure flow-guided DSR framework, where a cross-modality flow map is learned to guide the RGB-structure information transferring for precise depth upsampling. Specifically, our framework consists of a cross-modality flow-guided upsampling network (CFUNet) and a flow-enhanced pyramid edge attention network (PEANet). CFUNet contains a trilateral self-attention module combining both the geometric and semantic correlations for reliable cross-modality flow learning. Then, the learned flow maps are combined with the grid-sampling mechanism for coarse high-resolution (HR) depth prediction. PEANet targets at integrating the learned flow map as the edge attention into a pyramid network to hierarchically learn the edge-focused guidance feature for depth edge refinement. Extensive experiments on real and synthetic DSR datasets verify that our approach achieves excellent performance compared to state-of-the-art methods.

翻译：与合成环境不同,真正的深度超分辨率(DSR)与合成环境不同,由于真实世界低分辨率(LR)深度地图中自然退化造成的结构扭曲和边缘噪音,是一项具有挑战性的任务,因为实际世界低分辨率(LR)深度地图中自然退化造成的结构扭曲和边缘噪音,导致深度地图和RGB引线引线引线引线引线在结构上出现重大不一致,这有可能混淆RGB结构结构结构指导准则,从而降低DSR的质量。在本文件中,我们提出了一个新的结构流向流向和定线导DSR框架,在这个框架中,学习了一种跨模式流向跨模式流向流源学习的新结构,用以指导RGB结构结构信息的传输,以便进行精确的深度调查。具体来说,我们的框架包括一个跨模式流向流向上导的扫描网络(CFEUNet)和一个流动流图,作为高强度的流向流向流向流向流向流流向流向流向流向流,作为我们高分辨率深度分析的深度研究的深度分析,从而将我们高端的深度导航,从而将高端对高端实验的深度导航,从而进行高端实验,从而进行高端实验。