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

Object 6D pose estimation is a fundamental task in many applications.Conventional methods solve the task by detecting and matching the keypoints,then estimating the pose. Recent efforts bringing deep learning into theproblem mainly overcome the vulnerability of conventional methods toenvironmental variation due to the hand-crafted feature design. However, thesemethods cannot achieve end-to-end learning and good interpretability at thesame time. In this paper, we propose REDE, a novel end-to-end object poseestimator using RGB-D data, which utilizes network for keypoint regression, anda differentiable geometric pose estimator for pose error back-propagation.Besides, to achieve better robustness when outlier keypoint prediction occurs,we further propose a differentiable outliers elimination method that regressesthe candidate result and the confidence simultaneously. Via confidence weightedaggregation of multiple candidates, we can reduce the effect from the outliersin the final estimation. Finally, following the conventional method, we apply alearnable refinement process to further improve the estimation. Theexperimental results on three benchmark datasets show that REDE slightlyoutperforms the state-of-the-art approaches and is more robust to objectocclusion.

REDE: End-to-End Object 6D Pose Robust Estimation Using Differentiable Outliers Elimination