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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We present a robust and real-time monocular six degree of freedom visualrelocalization system. We use a Bayesian convolutional neural network toregress the 6-DOF camera pose from a single RGB image. It is trained in anend-to-end manner with no need of additional engineering or graph optimisation.The algorithm can operate indoors and outdoors in real time, taking under 6msto compute. It obtains approximately 2m and 6 degrees accuracy for very largescale outdoor scenes and 0.5m and 10 degrees accuracy indoors. Using a Bayesianconvolutional neural network implementation we obtain an estimate of themodel's relocalization uncertainty and improve state of the art localizationaccuracy on a large scale outdoor dataset. We leverage the uncertainty measureto estimate metric relocalization error and to detect the presence or absenceof the scene in the input image. We show that the model's uncertainty is causedby images being dissimilar to the training dataset in either pose orappearance.

Modelling Uncertainty in Deep Learning for Camera Relocalization