Saved in:
Bibliographic Details
Main Author: Castorena, Juan
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2401.15029
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866916106890903552
author Castorena, Juan
author_facet Castorena, Juan
contents This work leverages neural radiance fields and remote sensing for forestry applications. Here, we show neural radiance fields offer a wide range of possibilities to improve upon existing remote sensing methods in forest monitoring. We present experiments that demonstrate their potential to: (1) express fine features of forest 3D structure, (2) fuse available remote sensing modalities and (3), improve upon 3D structure derived forest metrics. Altogether, these properties make neural fields an attractive computational tool with great potential to further advance the scalability and accuracy of forest monitoring programs.
format Preprint
id arxiv_https___arxiv_org_abs_2401_15029
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Learning Neural Radiance Fields of Forest Structure for Scalable and Fine Monitoring
Castorena, Juan
Computer Vision and Pattern Recognition
This work leverages neural radiance fields and remote sensing for forestry applications. Here, we show neural radiance fields offer a wide range of possibilities to improve upon existing remote sensing methods in forest monitoring. We present experiments that demonstrate their potential to: (1) express fine features of forest 3D structure, (2) fuse available remote sensing modalities and (3), improve upon 3D structure derived forest metrics. Altogether, these properties make neural fields an attractive computational tool with great potential to further advance the scalability and accuracy of forest monitoring programs.
title Learning Neural Radiance Fields of Forest Structure for Scalable and Fine Monitoring
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2401.15029