  Depth estimation features are helpful for 3D recognition. Commodity-grade
depth cameras are able to capture depth and color image in real-time. However,
glossy, transparent or distant surface cannot be scanned properly by the
sensor. As a result, enhancement and restoration from sensing depth is an
important task. Depth completion aims at filling the holes that sensors fail to
detect, which is still a complex task for machine to learn. Traditional
hand-tuned methods have reached their limits, while neural network based
methods tend to copy and interpolate the output from surrounding depth values.
This leads to blurred boundaries, and structures of the depth map are lost.
Consequently, our main work is to design an end-to-end network improving
completion depth maps while maintaining edge clarity. We utilize self-attention
mechanism, previously used in image inpainting fields, to extract more useful
information in each layer of convolution so that the complete depth map is
enhanced. In addition, we propose boundary consistency concept to enhance the
depth map quality and structure. Experimental results validate the
effectiveness of our self-attention and boundary consistency schema, which
outperforms previous state-of-the-art depth completion work on Matterport3D
dataset. Our code is publicly available at
https://github.com/tsunghan-wu/Depth-Completion.
