PatchNeRF: localized neural radiance field training for city-scale aerial images

Landon Swartz; Taci Kucukpinar; Jaired Collins; Richard Massaro; Kannappan Palaniappan

doi:10.1117/12.3026753

10 June 2024 PatchNeRF: localized neural radiance field training for city-scale aerial images

Landon Swartz, Taci Kucukpinar, Jaired Collins, Richard Massaro, Kannappan Palaniappan

Proceedings Volume PC13037, Geospatial Informatics XIV; PC1303703 (2024) https://doi.org/10.1117/12.3026753
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

In recent years, 3D reconstruction technology, especially for mapping entire cities, has made great strides. This technology is crucial for detailed mapping and observation of cities. However, accurately capturing small objects like buildings from aerial images remains challenging. Traditional methods struggle to balance the entire city structure with fine details of building. A new technique, Neural Radiance Fields (NeRF), offers a way to create detailed scene views from set camera positions, but it is not efficient for large areas like cities. To solve this, we developed PatchNeRF. This method improves NeRF by focusing on specific areas of interest, allowing for more detailed and quicker results. PatchNeRF can repeatedly refine specific parts of a city model, like individual buildings, making it a big leap forward in creating detailed and efficient 3D city maps.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Landon Swartz, Taci Kucukpinar, Jaired Collins, Richard Massaro, and Kannappan Palaniappan "PatchNeRF: localized neural radiance field training for city-scale aerial images", Proc. SPIE PC13037, Geospatial Informatics XIV, PC1303703 (10 June 2024); https://doi.org/10.1117/12.3026753

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