Learning Hierarchical Color Guidance for Depth Map Super-Resolution
<|reference_start|>Learning Hierarchical Color Guidance for Depth Map Super-Resolution: Color information is the most commonly used prior knowledge for depth map super-resolution (DSR), which can provide high-frequency boundary guidance for detail restoration. However, its role and functionality in DSR have not been fully developed. In this paper, we rethink the utilization of color information and propose a hierarchical color guidance network to achieve DSR. On the one hand, the low-level detail embedding module is designed to supplement high-frequency color information of depth features in a residual mask manner at the low-level stages. On the other hand, the high-level abstract guidance module is proposed to maintain semantic consistency in the reconstruction process by using a semantic mask that encodes the global guidance information. The color information of these two dimensions plays a role in the front and back ends of the attention-based feature projection (AFP) module in a more comprehensive form. Simultaneously, the AFP module integrates the multi-scale content enhancement block and adaptive attention projection block to make full use of multi-scale information and adaptively project critical restoration information in an attention manner for DSR. Compared with the state-of-the-art methods on four benchmark datasets, our method achieves more competitive performance both qualitatively and quantitatively.<|reference_end|>
@article{cong2024learning,
    title={Learning Hierarchical Color Guidance for Depth Map Super-Resolution},
    author={Runmin Cong, Ronghui Sheng, Hao Wu, Yulan Guo, Yunchao Wei, Wangmeng
  Zuo, Yao Zhao, and Sam Kwong},
    journal={arXiv preprint arXiv:2403.07290},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07290},
    primaryClass={cs.CV}
}