On end-to-end 6DOF object pose estimation and robustness to object scale

Georgios Nikolaos Albanis, Nikolaos Zioulis, Anargyros Chatzitofis, Anastasios Dimou, Dimitrios Zarpalas, Petros Daras

Keywords: Drone pose estimation, Differentiable PnP, Object pose estimation

Abstract:

This report contains a set of experiments that seek to reproduce the claims of two recent works related to keypoint estimation, one specific to 6DOF object pose estimation, and the other presenting a generic architectural improvement for keypoint estimation but demonstrated in human pose estimation.
More specifically, in the backpropagatable PnP , the authors claim that incorporating geometric optimization in a deep-learning pipeline and predicting an object’s pose in an end-to-end manner yields improved performance.
On the other hand, HigherHRNet introduces a novel heatmap aggregation method that allows for scale-aware pose estimations, offering higher keypoint localization accuracy for small scale objects.

Paper Url: https://openreview.net/forum?id=fgUXyn4Qrk0&noteId=n4yKe2VVmN3&referrer=%5BML%20Reproducibility%20Challenge%202020%5D(%2Fgroup%3Fid%3DML_Reproducibility_Challenge%2F2020)