Mask-adaptive Gated Convolution and Bi-directional Progressive Fusion
  Network for Depth Completion
Tingxuan Huang Jiacheng Miao Shizhuo Deng Tong Dongyue Chen
Depth completion is a critical task for handling depth images with missing
pixels, which can negatively impact further applications. Recent approaches
have utilized Convolutional Neural Networks (CNNs) to reconstruct depth images
with the assistance of color images. However, vanilla convolution has
non-negligible drawbacks in handling missing pixels. To solve this problem, we
propose a new model for depth completion based on an encoder-decoder structure.
Our model introduces two key components: the Mask-adaptive Gated Convolution
(MagaConv) architecture and the Bi-directional Progressive Fusion (BP-Fusion)
module. The MagaConv architecture is designed to acquire precise depth features
by modulating convolution operations with iteratively updated masks, while the
BP-Fusion module progressively integrates depth and color features, utilizing
consecutive bi-directional fusion structures in a global perspective. Extensive
experiments on popular benchmarks, including NYU-Depth V2, DIML, and SUN RGB-D,
demonstrate the superiority of our model over state-of-the-art methods. We
achieved remarkable performance in completing depth maps and outperformed
existing approaches in terms of accuracy and reliability.