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

Modeling large-scale scenes from unconstrained image collections in-the-wildhas proven to be a major challenge in computer vision. Existing methodstackling in-the-wild neural rendering operate in a closed-world setting, whereknowledge is limited to a scene's captured images within a training set. Wepropose EvE, which is, to the best of our knowledge, the first methodleveraging generative priors to improve in-the-wild scene modeling. We employpre-trained generative networks to enrich K-Planes representations withextrinsic knowledge. To this end, we define an alternating training procedureto conduct optimization guidance of K-Planes trained on the training set. Wecarry out extensive experiments and verify the merit of our method on syntheticdata as well as real tourism photo collections. EvE enhances rendered sceneswith richer details and outperforms the state of the art on the task of novelview synthesis in-the-wild. Our project page can be found athttps://eve-nvs.github.io .

EvE: Exploiting Generative Priors for Radiance Field Enrichment