  Decoders play significant roles in recovering scene depths. However, the
decoders used in previous works ignore the propagation of multilevel lossless
fine-grained information, cannot adaptively capture local and global
information in parallel, and cannot perform sufficient global statistical
analyses on the final output disparities. In addition, the process of mapping
from a low-resolution feature space to a high-resolution feature space is a
one-to-many problem that may have multiple solutions. Therefore, the quality of
the recovered depth map is low. To this end, we propose a high-quality decoder
(HQDec), with which multilevel near-lossless fine-grained information, obtained
by the proposed adaptive axial-normalized position-embedded channel attention
sampling module (AdaAxialNPCAS), can be adaptively incorporated into a
low-resolution feature map with high-level semantics utilizing the proposed
adaptive information exchange scheme. In the HQDec, we leverage the proposed
adaptive refinement module (AdaRM) to model the local and global dependencies
between pixels in parallel and utilize the proposed disparity attention module
to model the distribution characteristics of disparity values from a global
perspective. To recover fine-grained high-resolution features with maximal
accuracy, we adaptively fuse the high-frequency information obtained by
constraining the upsampled solution space utilizing the local and global
dependencies between pixels into the high-resolution feature map generated from
the nonlearning method. Extensive experiments demonstrate that each proposed
component improves the quality of the depth estimation results over the
baseline results, and the developed approach achieves state-of-the-art results
on the KITTI and DDAD datasets. The code and models will be publicly available
at \href{https://github.com/fwucas/HQDec}{HQDec}.
