AFN: Attentional Feedback Network based 3D Terrain Super-Resolution
This work addresses the need for high-resolution terrain details in applications like simulations and computer graphics, representing an incremental improvement in DEM super-resolution by integrating multimodal data with attention mechanisms.
The paper tackles the problem of increasing the resolution of low-resolution Digital Elevation Models (LRDEMs) by proposing a novel fully convolutional neural network architecture called Attentional Feedback Network (AFN), which uses an attention-based feedback mechanism to fuse information from LRDEMs and aerial images, resulting in outperforming existing state-of-the-art methods in accuracy and realism.
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.