论文标题
AFN：基于注意力反馈网络的3D地形超分辨率
AFN: Attentional Feedback Network based 3D Terrain Super-Resolution
论文作者
论文摘要
代表地面特征的地形在许多应用中都起着至关重要的作用，例如模拟，路线计划，表面动态分析，基于计算机图形的游戏，娱乐，电影，等等。随着数字技术的最新进展，这些应用程序需要在地形中存在高分辨率的细节。在本文中，我们提出了一种新型的完全卷积神经网络的超分辨率体系结构，以提高低分辨率数字高程模型（LRDEM）的分辨率，借助从相应的空中图像中提取的信息作为互补方式。我们使用名为“注意反馈网络”（AFN）的基于注意力的反馈机制（AFN）进行LRDEM的超分辨率，该机制有选择地融合了LRDEM和空中图像的信息，以增强和注入高频特征并实际生产地形。我们将所提出的体系结构与现有的最新DEM超级分辨率方法进行比较，并表明所提出的体系结构以准确和现实的方式优于增强输入LRDEM的分辨率。
Terrain, representing features of an earth surface, plays a crucial role in many applications such as simulations, route planning, analysis of surface dynamics, computer graphics-based games, entertainment, films, to name a few. With recent advancements in digital technology, these applications demand the presence of high-resolution details in the terrain. In this paper, we propose a novel fully convolutional neural network-based super-resolution architecture to increase the resolution of low-resolution Digital Elevation Model (LRDEM) with the help of information extracted from the corresponding aerial image as a complementary modality. We perform the super-resolution of LRDEM using an attention-based feedback mechanism named 'Attentional Feedback Network' (AFN), which selectively fuses the information from LRDEM and aerial image to enhance and infuse the high-frequency features and to produce the terrain realistically. We compare the proposed architecture with existing state-of-the-art DEM super-resolution methods and show that the proposed architecture outperforms enhancing the resolution of input LRDEM accurately and in a realistic manner.
