# AFN: Attentional Feedback Network based 3D Terrain Super-Resolution

arXiv: https://arxiv.org/abs/2010.01626 · HTML: https://mlsanity.com/paper/2010.01626/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Attention based Occlusion Removal for Hybrid Telepresence Systems](https://mlsanity.com/paper/2112.01098/) (2021)
- [Deep Generative Framework for Interactive 3D Terrain Authoring and Manipulation](https://mlsanity.com/paper/2201.02369/) (2022)

## Abstract (arXiv)

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.