Structure Flow-Guided Network for Real Depth Super-Resolution

• URL: http://arxiv.org/abs/2301.13416v1
• Date: Tue, 31 Jan 2023 05:13:55 GMT
• Title: Structure Flow-Guided Network for Real Depth Super-Resolution
• Authors: Jiayi Yuan, Haobo Jiang, Xiang Li, Jianjun Qian, Jun Li, Jian Yang
• Abstract summary: We propose a novel structure flow-guided depth super-resolution (DSR) framework. A cross-modality flow map is learned to guide the RGB-structure information transferring for precise depth upsampling. Our framework achieves excellent performance compared to state-of-the-art methods.
• Score: 28.63334760296165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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