Learning Hierarchical Color Guidance for Depth Map Super-Resolution
Runmin Cong Ronghui Sheng Hao Wu Yulan Guo Yunchao Wei Wangmeng Zuo Yao Zhao Sam Kwong
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.