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

Visual relocalization has been a widely discussed problem in 3D vision: givena pre-constructed 3D visual map, the 6 DoF (Degrees-of-Freedom) pose of a queryimage is estimated. Relocalization in large-scale indoor environments enablesattractive applications such as augmented reality and robot navigation.However, appearance changes fast in such environments when the camera moves,which is challenging for the relocalization system. To address this problem, wepropose a virtual view synthesis-based approach, RenderNet, to enrich thedatabase and refine poses regarding this particular scenario. Instead ofrendering real images which requires high-quality 3D models, we opt to directlyrender the needed global and local features of virtual viewpoints and applythem in the subsequent image retrieval and feature matching operationsrespectively. The proposed method can largely improve the performance inlarge-scale indoor environments, e.g., achieving an improvement of 7.1\% and12.2\% on the Inloc dataset.

RenderNet: Visual Relocalization Using Virtual Viewpoints in Large-Scale Indoor Environments