  Raw depth images captured in indoor scenarios frequently exhibit extensive
missing values due to the inherent limitations of the sensors and environments.
For example, transparent materials frequently elude detection by depth sensors;
surfaces may introduce measurement inaccuracies due to their polished textures,
extended distances, and oblique incidence angles from the sensor. The presence
of incomplete depth maps imposes significant challenges for subsequent vision
applications, prompting the development of numerous depth completion techniques
to mitigate this problem. Numerous methods excel at reconstructing dense depth
maps from sparse samples, but they often falter when faced with extensive
contiguous regions of missing depth values, a prevalent and critical challenge
in indoor environments. To overcome these challenges, we design a novel
two-branch end-to-end fusion network named RDFC-GAN, which takes a pair of RGB
and incomplete depth images as input to predict a dense and completed depth
map. The first branch employs an encoder-decoder structure, by adhering to the
Manhattan world assumption and utilizing normal maps from RGB-D information as
guidance, to regress the local dense depth values from the raw depth map. The
other branch applies an RGB-depth fusion CycleGAN, adept at translating RGB
imagery into detailed, textured depth maps while ensuring high fidelity through
cycle consistency. We fuse the two branches via adaptive fusion modules named
W-AdaIN and train the model with the help of pseudo depth maps. Comprehensive
evaluations on NYU-Depth V2 and SUN RGB-D datasets show that our method
significantly enhances depth completion performance particularly in realistic
indoor settings.
