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UAV Remote Sensing and 3D Reconstruction

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (30 August 2024) | Viewed by 1285

Special Issue Editors


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Guest Editor
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
Interests: AI

E-Mail Website
Guest Editor
College of Artificial Intelligence, Southwest University, Chongqing 400715, China
Interests: image enhancement; image restoration; deep learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the field of computer vision has witnessed a number of transformative breakthroughs thanks to the advent of Neural Radiance Fields (Nerf), a revolutionary technique that has redefined the way we perceive and reconstruct three-dimensional scenes. This Special Issue delves into the intriguing realm of Nerf-based scene reconstruction which is specifically tailored towards Unmanned Aerial Vehicles (UAVs). As UAVs continue to proliferate across various domains, from cinematography to surveillance, the ability to reconstruct detailed and accurate 3D models of the environments in which they operate becomes paramount.

Our exploration encompasses the underlying principles of Nerf and its adaptation to the unique challenges posed by UAV-generated data. We explore the fusion of vision and robotics, unlocking the potential for real-time, high-fidelity scene reconstruction from aerial perspectives. The utilization of Nerf in UAV applications not only enhances our spatial understanding of the environment but also holds promise for their implementation within mapping, navigation, and disaster response.

This Special Issue aims to bring together researchers, practitioners, and enthusiasts to share their insights, methodologies, and advancements in the domain of Nerf-based UAV 3D reconstruction. By fostering collaboration and the exchange of ideas, we aspire to push the boundaries of this burgeoning field and pave the way for innovative applications that harness the synergy between Nerf and Unmanned Aerial Vehicles.

Prof. Dr. Shibiao Xu
Dr. Yun Liu
Dr. Honggang Chen
Guest Editors

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Keywords

  • neural radiance fields (NeRF)
  • UAV image processing
  • UAV scene reconstruction

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Published Papers (1 paper)

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Research

16 pages, 19990 KiB  
Article
Implicit–Explicit Coupling Enhancement for UAV Scene 3D Reconstruction
by Xiaobo Lin and Shibiao Xu
Appl. Sci. 2024, 14(6), 2425; https://doi.org/10.3390/app14062425 - 13 Mar 2024
Viewed by 976
Abstract
In unmanned aerial vehicle (UAV) large-scale scene modeling, challenges such as missed shots, low overlap, and data gaps due to flight paths and environmental factors, such as variations in lighting, occlusion, and weak textures, often lead to incomplete 3D models with blurred geometric [...] Read more.
In unmanned aerial vehicle (UAV) large-scale scene modeling, challenges such as missed shots, low overlap, and data gaps due to flight paths and environmental factors, such as variations in lighting, occlusion, and weak textures, often lead to incomplete 3D models with blurred geometric structures and textures. To address these challenges, an implicit–explicit coupling enhancement for a UAV large-scale scene modeling framework is proposed. Benefiting from the mutual promotion of implicit and explicit models, we initially address the issue of missing co-visibility clusters caused by environmental noise through large-scale implicit modeling with UAVs. This enhances the inter-frame photometric and geometric consistency. Subsequently, we enhance the multi-view point cloud reconstruction density via synthetic co-visibility clusters, effectively recovering missing spatial information and constructing a more complete dense point cloud. Finally, during the mesh modeling phase, high-quality 3D modeling of large-scale UAV scenes is achieved by inversely radiating and mapping additional texture details into 3D voxels. The experimental results demonstrate that our method achieves state-of-the-art modeling accuracy across various scenarios, outperforming existing commercial UAV aerial photography software (COLMAP 3.9, Context Capture 2023, PhotoScan 2023, Pix4D 4.5.6) and related algorithms. Full article
(This article belongs to the Special Issue UAV Remote Sensing and 3D Reconstruction)
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