Guided depth super-resolution (GDSR) involves restoring missing depth details
using the high-resolution RGB image of the same scene. Previous approaches have
struggled with the heterogeneity and complementarity of the multi-modal inputs,
and neglected the issues of modal misalignment, geometrical misalignment, and
feature selection. In this study, we rethink some essential components in GDSR
networks and propose a simple yet effective Dynamic Dual Alignment and
Aggregation network (D2A2). D2A2 mainly consists of 1) a dynamic dual alignment
module that adapts to alleviate the modal misalignment via a learnable domain
alignment block and geometrically align cross-modal features by learning the
offset; and 2) a mask-to-pixel feature aggregate module that uses the gated
mechanism and pixel attention to filter out irrelevant texture noise from RGB
features and combine the useful features with depth features. By combining the
strengths of RGB and depth features while minimizing disturbance introduced by
the RGB image, our method with simple reuse and redesign of basic components
achieves state-of-the-art performance on multiple benchmark datasets. The code
is available at this https URL

通过重新审视 GDSR 网络的一些关键组件并提出简单但有效的 Dynamic Dual Alignment and Aggregation 网络（D2A2），我们结合 RGB 和深度特征的优势，同时最小化 RGB 图像引入的干扰，通过对模态不匹配进行学习的领域对齐块和学习偏移来减轻模态不匹配的动态双重对齐模块，并通过使用门控机制和像素注意力来过滤 IR 纹理噪声并将有用的特征与深度特征结合起来的掩码到像素特征聚合模块，我们的方法在多个基准数据集上实现了最先进的性能。