深度图超分辨率重建研究综述

虽然高质量高分辨率的深度图能够显著地提高各种自然场景计算机视觉任务的性能，但是深度相机硬件的限制使得消费级深度相机拍摄到的深度图存在分辨率低、质量差和无效空洞等问题。深度图超分辨率重建（depth super-resolution reconstruction，DSR）是一种能有效提高深度图分辨率和质量的技术，并且DSR已经成为计算机视觉领域的研究热点。首先将介绍DSR的定义和近几年国内外DSR算法的研究进展，然后对深度学习DSR重建算法进行重点阐述与分析。接下来，将介绍深度图像质量评估准则。最后，对DSR的应用领域和未来所面对的挑战和机遇进行展望。

Review of depth map super-resolution reconstruction research

Although high-quality and high-resolution depth maps can significantly improve the performance of computer vision tasks in various natural scenes, the limitations of depth camera hardware make the depth maps captured by consumer-level depth cameras have problems such as low resolution, poor quality, invalid holes, etc. Depth super-resolution reconstruction(DSR) is a kind of technology that can effectively improve the resolution and quality of depth maps, and DSR has become a research hot-spot in the field of computer vision. Firstly, this paper introduced the definition of DSR and the research progress of DSR algorithm at home and abroad in recent years, and then mainly stated and analyzed deep learning DSR reconstruction algorithms. Next, it introduced the depth image quality evaluation criteria. Finally, this paper prospected the application fields of DSR and the challenges as well as the opportunities in the future.