  Limited by the cost and technology, the resolution of depth map collected by
depth camera is often lower than that of its associated RGB camera. Although
there have been many researches on RGB image super-resolution (SR), a major
problem with depth map super-resolution is that there will be obvious jagged
edges and excessive loss of details. To tackle these difficulties, in this
work, we propose a multi-scale progressive fusion network for depth map SR,
which possess an asymptotic structure to integrate hierarchical features in
different domains. Given a low-resolution (LR) depth map and its associated
high-resolution (HR) color image, We utilize two different branches to achieve
multi-scale feature learning. Next, we propose a step-wise fusion strategy to
restore the HR depth map. Finally, a multi-dimensional loss is introduced to
constrain clear boundaries and details. Extensive experiments show that our
proposed method produces improved results against state-of-the-art methods both
qualitatively and quantitatively.
