Guided depth map super-resolution (GDSR), which aims to reconstruct a high-resolution depth map from a low-resolution observation with the help of a paired high-resolution color image, is a longstanding and fundamental problem that has attracted considerable attention from computer vision and image processing communities. Myriad novel and effective approaches have been proposed recently, especially with powerful deep learning techniques. This survey is an effort to present a comprehensive survey of recent progress in GDSR. We start by summarizing the problem of GDSR and explaining why it is challenging. Next, we introduce some commonly used datasets and image quality assessment methods. In addition, we roughly classify existing GDSR methods into three categories: filtering-based methods, prior-based methods, and learning-based methods. In each category, we introduce the general description of the published algorithms and design principles, summarize the representative methods, and discuss their highlights and limitations. Moreover, depth-related applications are introduced. Furthermore, we conduct experiments to evaluate the performance of some representative methods based on unified experimental configurations, so as to offer a systematic and fair performance evaluation to readers. Finally, weconclude this survey with possible directions and open problems for further research. All related materials can be found at https://github.com/zhwzhong/Guided-Depth-Map-Super-resolution-A-Survey.

引导深度图超分辨率（GDSR）旨在借助配对的高分辨率彩色图像从低分辨率观察中重建高分辨率深度图，是一个长期存在的基本问题，引起了人们的广泛关注。计算机视觉和图像处理社区。最近提出了无数新颖且有效的方法，尤其是强大的深度学习技术。本调查旨在对 GDSR 的最新进展进行全面调查。我们首先总结 GDSR 的问题并解释为什么它具有挑战性。接下来，我们介绍一些常用的数据集和图像质量评估方法。此外，我们将现有的GDSR方法大致分为三类：基于过滤的方法、基于先验的方法和基于学习的方法。在每个类别中，我们介绍了已发表的算法和设计原理的一般描述，总结了代表性方法，并讨论了它们的亮点和局限性。此外，还介绍了与深度相关的应用。此外，我们还基于统一的实验配置对一些代表性方法的性能进行了实验评估，以便为读者提供系统、公平的性能评估。最后，我们总结了本次调查，提出了进一步研究的可能方向和开放问题。所有相关资料可以在https://github.com/zhwzhong/Guided-Depth-Map-Super-resolution-A-Survey找到。介绍了与深度相关的应用。此外，我们还基于统一的实验配置对一些代表性方法的性能进行了实验评估，以便为读者提供系统、公平的性能评估。最后，我们总结了本次调查，提出了进一步研究的可能方向和开放问题。所有相关资料可以在https://github.com/zhwzhong/Guided-Depth-Map-Super-resolution-A-Survey找到。介绍了与深度相关的应用。此外，我们还基于统一的实验配置对一些代表性方法的性能进行了实验评估，以便为读者提供系统、公平的性能评估。最后，我们总结了本次调查，提出了进一步研究的可能方向和开放问题。所有相关资料可以在https://github.com/zhwzhong/Guided-Depth-Map-Super-resolution-A-Survey找到。