5 papers
DGCM-Net: Dense Geometrical Correspondence Matching Network for Incremental Experience-based Robotic Grasping
Timothy Patten, Kiru Park, Markus Vincze
This article presents a method for grasping novel objects by learning from experience. Successful attempts are remembered and then used to guide future grasps such that more reliab…
Addressing the Sim2Real Gap in Robotic 3D Object Classification
Jean-Baptiste Weibel, Timothy Patten, Markus Vincze
Object classification with 3D data is an essential component of any scene understanding method. It has gained significant interest in a variety of communities, most notably in robo…
VeREFINE: Integrating Object Pose Verification with Physics-guided Iterative Refinement
Dominik Bauer, Timothy Patten, Markus Vincze
Accurate and robust object pose estimation for robotics applications requires verification and refinement steps. In this work, we propose to integrate hypotheses verification with…
Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation
Kiru Park, Timothy Patten, Markus Vincze
Estimating the 6D pose of objects using only RGB images remains challenging because of problems such as occlusion and symmetries. It is also difficult to construct 3D models with p…
EasyLabel: A Semi-Automatic Pixel-wise Object Annotation Tool for Creating Robotic RGB-D Datasets
Markus Suchi, Timothy Patten, David Fischinger +1
Developing robot perception systems for recognizing objects in the real-world requires computer vision algorithms to be carefully scrutinized with respect to the expected operating…