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Guided Depth Map Super-Resolution: A Survey
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DOI：10.1145/3584860.png)
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Abstract
En 中文
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 learningbased methods. In each category, we introduce the general description of the published algorithms and design principles, summarize the representativemethods, 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/GuidedDepth-Map-Super-resolution-A-Survey.
Keywords:
Guided depth map super-resolution
survey
filtering
prior
learning
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