Paper on “6D Pose Estimation” accepted by BMVC 2018

Paper on “6D Pose Estimation” accepted by BMVC 2018:

Congratulations to Chaitanya Mitash for the paper:

“Robust 6D Pose Estimation with Stochastic Congruent Sets”
by Chaitanya Mitash, Abdeslam Boularias and Kostas E. Bekris

which has been accepted to appear at the upcoming British Machine Vision (BMVC) conference, taking place this coming September in Newcastle, UK.

This work proposes a novel stochastic optimization process that treats the segmentation output of CNNs as a confidence probability. The algorithm, called Stochastic Congruent Sets (STOCS), samples pointsets on the point cloud according to the soft segmentation distribution and so as to agree with the object’s known geometry. The pointsets are then matched to congruent sets on the 3D object model to generate pose estimates. STOCS is shown to be robust on an APC dataset, despite the fact the CNN is trained only on synthetic data. In the YCB dataset, STOCS outperforms a recent network for 6D pose estimation and alternative pointset matching techniques.

BMVC 2018 website: