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

Perspective-n-Point-and-Line (P$n$PL) algorithms aim at fast, accurate, androbust camera localization with respect to a 3D model from 2D-3D featurecorrespondences, being a major part of modern robotic and AR/VR systems.Current point-based pose estimation methods use only 2D feature detectionuncertainties, and the line-based methods do not take uncertainties intoaccount. In our setup, both 3D coordinates and 2D projections of the featuresare considered uncertain. We propose PnP(L) solvers based on EPnP and DLS forthe uncertainty-aware pose estimation. We also modify motion-only bundleadjustment to take 3D uncertainties into account. We perform exhaustivesynthetic and real experiments on two different visual odometry datasets. Thenew PnP(L) methods outperform the state-of-the-art on real data in isolation,showing an increase in mean translation accuracy by 18% on a representativesubset of KITTI, while the new uncertain refinement improves pose accuracy formost of the solvers, e.g. decreasing mean translation error for the EPnP by 16%compared to the standard refinement on the same dataset. The code is availableat https://alexandervakhitov.github.io/uncertain-pnp/.

Uncertainty-Aware Camera Pose Estimation from Points and Lines