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.

基于编码器-解码器结构的新型深度补全模型提出了两个关键组件：MagaConv架构和BP-Fusion模块。其中，MagaConv架构通过使用迭代更新的遮罩调制卷积运算来获取精确的深度特征，而BP-Fusion模块通过在全局视角中利用连续的双向融合结构逐步整合深度和颜色特征。通过在包括NYU-Depth V2，DIML和SUN RGB-D在内的流行基准数据集上进行广泛实验，我们的模型表现优越，成就了在深度图像补全方面的卓越性能，并在准确性和可靠性方面超越了现有方法。

面具适应性门控卷积和双向渐进融合网络用于深度补全