AGG-Net: Attention Guided Gated-convolutional Network for Depth Image
  Completion
Dongyue Chen Tingxuan Huang Zhimin Song Shizhuo Deng Tong Jia
Recently, stereo vision based on lightweight RGBD cameras has been widely
used in various fields. However, limited by the imaging principles, the
commonly used RGB-D cameras based on TOF, structured light, or binocular vision
acquire some invalid data inevitably, such as weak reflection, boundary
shadows, and artifacts, which may bring adverse impacts to the follow-up work.
In this paper, we propose a new model for depth image completion based on the
Attention Guided Gated-convolutional Network (AGG-Net), through which more
accurate and reliable depth images can be obtained from the raw depth maps and
the corresponding RGB images. Our model employs a UNet-like architecture which
consists of two parallel branches of depth and color features. In the encoding
stage, an Attention Guided Gated-Convolution (AG-GConv) module is proposed to
realize the fusion of depth and color features at different scales, which can
effectively reduce the negative impacts of invalid depth data on the
reconstruction. In the decoding stage, an Attention Guided Skip Connection
(AG-SC) module is presented to avoid introducing too many depth-irrelevant
features to the reconstruction. The experimental results demonstrate that our
method outperforms the state-of-the-art methods on the popular benchmarks
NYU-Depth V2, DIML, and SUN RGB-D.