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

Camera relocalization has various applications in autonomous driving.Previous camera pose regression models consider only ideal scenarios wherethere is little environmental perturbation. To deal with challenging drivingenvironments that may have changing seasons, weather, illumination, and thepresence of unstable objects, we propose RobustLoc, which derives itsrobustness against perturbations from neural differential equations. Our modeluses a convolutional neural network to extract feature maps from multi-viewimages, a robust neural differential equation diffusion block module to diffuseinformation interactively, and a branched pose decoder with multi-layertraining to estimate the vehicle poses. Experiments demonstrate that RobustLocsurpasses current state-of-the-art camera pose regression models and achievesrobust performance in various environments. Our code is released at:https://github.com/sijieaaa/RobustLoc

RobustLoc: Robust Camera Pose Regression in Challenging Driving Environments