5 citations · 5 across the 3 of their papers we have counts for
6 papers
Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks
Jianxiang Feng, Maximilian Durner, Zoltan-Csaba Marton +2
This work focuses on improving uncertainty estimation in the field of object classification from RGB images and demonstrates its benefits in two robotic applications. We employ a (…
Unknown Object Segmentation from Stereo Images
Maximilian Durner, Wout Boerdijk, Martin Sundermeyer +3
Although instance-aware perception is a key prerequisite for many autonomous robotic applications, most of the methods only partially solve the problem by focusing solely on known…
"What's This?" -- Learning to Segment Unknown Objects from Manipulation Sequences
Wout Boerdijk, Martin Sundermeyer, Maximilian Durner +1
We present a novel framework for self-supervised grasped object segmentation with a robotic manipulator. Our method successively learns an agnostic foreground segmentation followed…
Self-Supervised Object-in-Gripper Segmentation from Robotic Motions
Wout Boerdijk, Martin Sundermeyer, Maximilian Durner +1
Accurate object segmentation is a crucial task in the context of robotic manipulation. However, creating sufficient annotated training data for neural networks is particularly time…
Multi-path Learning for Object Pose Estimation Across Domains
Martin Sundermeyer, Maximilian Durner, En Yen Puang +4
We introduce a scalable approach for object pose estimation trained on simulated RGB views of multiple 3D models together. We learn an encoding of object views that does not only d…
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
Martin Sundermeyer, Zoltan-Csaba Marton, Maximilian Durner +2
We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that i…