Guided depth map super-resolution (GDSR), which aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image, is a longstanding and fundamental problem, it has attracted considerable attention from computer vision and image processing communities. A myriad of 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, i.e., 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, the 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, we conclude this survey with possible directions and open problems for further research. All the related materials can be found at \url{https://github.com/zhwzhong/Guided-Depth-Map-Super-resolution-A-Survey}.

翻译：引导式深度图超分辨（GDSR）旨在借助配对的低分辨率深度图与高分辨率彩色图像重建高分辨率深度图，是一个长期且基础的研究问题，吸引了计算机视觉与图像处理领域的广泛关注。近年来，大量新颖且高效的方法被提出，尤其是基于强大深度学习技术的方法。本文旨在对GDSR的最新进展进行全面综述。我们首先总结GDSR问题并阐释其挑战性。接着，介绍常用数据集与图像质量评估方法。此外，我们将现有GDSR方法大致分为三类：基于滤波的方法、基于先验的方法和基于学习的方法。在每一类中，介绍已发表算法的总体描述与设计原理，总结代表性方法，并探讨其优势与局限性。同时，介绍与深度相关的应用。进一步，我们基于统一实验配置对代表性方法进行性能评估，以向读者提供系统且公平的性能评价。最后，总结本综述中可能的未来研究方向与开放问题。所有相关资料可参见\url{https://github.com/zhwzhong/Guided-Depth-Map-Super-resolution-A-Survey}。