UseGeo - A UAV-based multi-sensor dataset for geospatial research

Nex, F.; Stathopoulou, E.K.; Remondino, F.; Yang, M.Y.; Madhuanand, L.; Yogender, Y.; Alsadik, B.; Weinmann, M.; Jutzi, B.; Qin, R.

doi:10.1016/j.ophoto.2024.100070

ISPRS Open Journal of Photogrammetry and Remote Sensing

2024

DOI: 10.1016/j.ophoto.2024.100070

|View full text |Cite

UseGeo - A UAV-based multi-sensor dataset for geospatial research

F. Nex,

E.K. Stathopoulou,

F. Remondino

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Leveraging Neural Radiance Fields for Large-Scale 3D Reconstruction from Aerial Imagery

Hermann,

Kwak,

Ruf

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Since conventional photogrammetric approaches struggle with with low-texture, reflective, and transparent regions, this study explores the application of Neural Radiance Fields (NeRFs) for large-scale 3D reconstruction of outdoor scenes, since NeRF-based methods have recently shown very impressive results in these areas. We evaluate three approaches: Mega-NeRF, Block-NeRF, and Direct Voxel Grid Optimization, focusing on their accuracy and completeness compared to ground truth point clouds. In addition, we analyze the effects of using multiple sub-modules, estimating the visibility by an additional neural network and varying the density threshold for the extraction of the point cloud. For performance evaluation, we use benchmark datasets that correspond to the setting off standard flight campaigns and therefore typically have nadir camera perspective and relatively little image overlap, which can be challenging for NeRF-based approaches that are typically trained with significantly more images and varying camera angles. We show that despite lower quality compared to classic photogrammetric approaches, NeRF-based reconstructions provide visually convincing results in challenging areas. Furthermore, our study shows that in particular increasing the number of sub-modules and predicting the visibility using an additional neural network improves the quality of the resulting reconstructions significantly.

show abstract

Leveraging Neural Radiance Fields for Large-Scale 3D Reconstruction from Aerial Imagery

Hermann,

Kwak,

Ruf

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

UseGeo - A UAV-based multi-sensor dataset for geospatial research

Cited by 1 publication

References 30 publications

Leveraging Neural Radiance Fields for Large-Scale 3D Reconstruction from Aerial Imagery

Leveraging Neural Radiance Fields for Large-Scale 3D Reconstruction from Aerial Imagery

Contact Info

Product

Resources

About