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:旨在从低分辨率(LR)观测中重建高分辨率(HR)深度地图,并配对HR彩色图像,这是一个长期和根本的问题,它引起了计算机视觉和图像处理界的极大关注。最近提出了许多新颖和有效的方法,特别是强有力的深层学习技术。这次调查的目的是全面调查全球数据采集和反应的最新进展。我们首先总结全球数据采集和反应问题,并解释其具有挑战性的原因。接下来,我们推出一些常用的数据集和图像质量评估方法。此外,我们大致将现有的全球数据采集和反应方法分为三类,即基于过滤的方法、基于先前的方法和基于学习的方法。我们在每个类别中都介绍了已公布的算法和设计原则的一般性描述,总结了代表性方法,并讨论了其亮点和局限性。此外,我们还进行了一些实验,以评价一些基于统一实验配置的代表性方法的性能,以便向读者提供系统和公平的业绩评估。最后,我们通过在公开的版本/SDB/SB/SB/SB/SUB/SUB/SUB/SUDA/SUDSUDA/SUDA/SUDRDA/SUDRDRDA/SUDR/SUDRDR/SUDRDRDRDRDRDRDRDR/C/CRDRDRRRRRRRDRDRDRDRDRDRDRRRRRRRRR) 找到可能的可能的可能的可能的可能的可能的可能的可能的可能方向和可能方向和可能的指南。我们。我们作出结论性研究。最后结论。我们。我们找到了可能的指南。我们。我们为研究方向和可能的指南。我们找到了可能的指南和可能的指南。我们找到的指南。我们找到的指南。我们。我们找到的指南和可能的指南。我们找到的指南。我们找到的指南和与研究/FDBDBDBDBDBDBDBDBDBD/C/C/C/C/C/C/C/FD/C/C/C/C/C/C/C/C/CRDRDRDRDRDRDRDRDRDRD/C/C/C/C/C/