English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present Surf-NeRF, a modified implementation of the recently introducedShadow Neural Radiance Field (S-NeRF) model. This method is able to synthesizenovel views from a sparse set of satellite images of a scene, while accountingfor the variation in lighting present in the pictures. The trained model canalso be used to accurately estimate the surface elevation of the scene, whichis often a desirable quantity for satellite observation applications. S-NeRFimproves on the standard Neural Radiance Field (NeRF) method by considering theradiance as a function of the albedo and the irradiance. Both these quantitiesare output by fully connected neural network branches of the model, and thelatter is considered as a function of the direct light from the sun and thediffuse color from the sky. The implementations were run on a dataset ofsatellite images, augmented using a zoom-and-crop technique. A hyperparameterstudy for NeRF was carried out, leading to intriguing observations on themodel's convergence. Finally, both NeRF and S-NeRF were run until 100k epochsin order to fully fit the data and produce their best possible predictions. Thecode related to this article can be found athttps://github.com/fsemerar/surfnerf.

NeRF applied to satellite imagery for surface reconstruction