4 code implementations • 15 Sep 2020 • Tomas Hodan, Martin Sundermeyer, Bertram Drost, Yann Labbe, Eric Brachmann, Frank Michel, Carsten Rother, Jiri Matas
This paper presents the evaluation methodology, datasets, and results of the BOP Challenge 2020, the third in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB-D image.
no code implementations • 9 Oct 2018 • Tomas Hodan, Rigas Kouskouridas, Tae-Kyun Kim, Federico Tombari, Kostas Bekris, Bertram Drost, Thibault Groueix, Krzysztof Walas, Vincent Lepetit, Ales Leonardis, Carsten Steger, Frank Michel, Caner Sahin, Carsten Rother, Jiri Matas
The workshop featured four invited talks, oral and poster presentations of accepted workshop papers, and an introduction of the BOP benchmark for 6D object pose estimation.
1 code implementation • ECCV 2018 • Tomas Hodan, Frank Michel, Eric Brachmann, Wadim Kehl, Anders Glent Buch, Dirk Kraft, Bertram Drost, Joel Vidal, Stephan Ihrke, Xenophon Zabulis, Caner Sahin, Fabian Manhardt, Federico Tombari, Tae-Kyun Kim, Jiri Matas, Carsten Rother
We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image.
no code implementations • CVPR 2017 • Alexander Krull, Eric Brachmann, Sebastian Nowozin, Frank Michel, Jamie Shotton, Carsten Rother
In this work we propose to learn an efficient algorithm for the task of 6D object pose estimation.
no code implementations • CVPR 2017 • Frank Michel, Alexander Kirillov, Eric Brachmann, Alexander Krull, Stefan Gumhold, Bogdan Savchynskyy, Carsten Rother
Most modern approaches solve this task in three steps: i) Compute local features; ii) Generate a pool of pose-hypotheses; iii) Select and refine a pose from the pool.
4 code implementations • CVPR 2017 • Eric Brachmann, Alexander Krull, Sebastian Nowozin, Jamie Shotton, Frank Michel, Stefan Gumhold, Carsten Rother
The most promising approach is inspired by reinforcement learning, namely to replace the deterministic hypothesis selection by a probabilistic selection for which we can derive the expected loss w. r. t.
no code implementations • CVPR 2016 • Eric Brachmann, Frank Michel, Alexander Krull, Michael Ying Yang, Stefan Gumhold, Carsten Rother
In recent years, the task of estimating the 6D pose of object instances and complete scenes, i. e. camera localization, from a single input image has received considerable attention.
no code implementations • ICCV 2015 • Alexander Krull, Eric Brachmann, Frank Michel, Michael Ying Yang, Stefan Gumhold, Carsten Rother
This is done by describing the posterior density of a particular object pose with a convolutional neural network (CNN) that compares an observed and rendered image.