Symmetric Uncertainty-Aware Feature Transmission for Depth
  Super-Resolution
Wuxuan Shi Mang Ye Bo Du
Color-guided depth super-resolution (DSR) is an encouraging paradigm that
enhances a low-resolution (LR) depth map guided by an extra high-resolution
(HR) RGB image from the same scene. Existing methods usually use interpolation
to upscale the depth maps before feeding them into the network and transfer the
high-frequency information extracted from HR RGB images to guide the
reconstruction of depth maps. However, the extracted high-frequency information
usually contains textures that are not present in depth maps in the existence
of the cross-modality gap, and the noises would be further aggravated by
interpolation due to the resolution gap between the RGB and depth images. To
tackle these challenges, we propose a novel Symmetric Uncertainty-aware Feature
Transmission (SUFT) for color-guided DSR. (1) For the resolution gap, SUFT
builds an iterative up-and-down sampling pipeline, which makes depth features
and RGB features spatially consistent while suppressing noise amplification and
blurring by replacing common interpolated pre-upsampling. (2) For the
cross-modality gap, we propose a novel Symmetric Uncertainty scheme to remove
parts of RGB information harmful to the recovery of HR depth maps. Extensive
experiments on benchmark datasets and challenging real-world settings suggest
that our method achieves superior performance compared to state-of-the-art
methods. Our code and models are available at
https://github.com/ShiWuxuan/SUFT.