5 papers
A Hybrid Approach for 6DoF Pose Estimation
Rebecca König, Bertram Drost
We propose a method for 6DoF pose estimation of rigid objects that uses a state-of-the-art deep learning based instance detector to segment object instances in an RGB image, follow…
BOP Challenge 2020 on 6D Object Localization
Tomas Hodan, Martin Sundermeyer, Bertram Drost +5
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…
A Summary of the 4th International Workshop on Recovering 6D Object Pose
Tomas Hodan, Rigas Kouskouridas, Tae-Kyun Kim +12
This document summarizes the 4th International Workshop on Recovering 6D Object Pose which was organized in conjunction with ECCV 2018 in Munich. The workshop featured four invited…
BOP: Benchmark for 6D Object Pose Estimation
Tomas Hodan, Frank Michel, Eric Brachmann +13
We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the obj…
Acquire, Augment, Segment & Enjoy: Weakly Supervised Instance Segmentation of Supermarket Products
Patrick Follmann, Bertram Drost, Tobias Böttger
Grocery stores have thousands of products that are usually identified using barcodes with a human in the loop. For automated checkout systems, it is necessary to count and classify…