26 citations · 32 across the 3 of their papers we have counts for
9 papers
Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects
Manuel Stoiber, Martin Sundermeyer, Rudolph Triebel
Tracking objects in 3D space and predicting their 6DoF pose is an essential task in computer vision. State-of-the-art approaches often rely on object texture to tackle this problem…
Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes
Martin Sundermeyer, Arsalan Mousavian, Rudolph Triebel +1
Grasping unseen objects in unconstrained, cluttered environments is an essential skill for autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning, ex…
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…
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…
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…