Search Results for author: Martin Sundermeyer

Found 15 papers, 10 papers with code

6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics

no code implementations23 Mar 2023 Maximilian Ulmer, Maximilian Durner, Martin Sundermeyer, Manuel Stoiber, Rudolph Triebel

We present a novel technique to estimate the 6D pose of objects from single images where the 3D geometry of the object is only given approximately and not as a precise 3D model.

6D Pose Estimation using RGB

BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects

no code implementations25 Feb 2023 Martin Sundermeyer, Tomas Hodan, Yann Labbe, Gu Wang, Eric Brachmann, Bertram Drost, Carsten Rother, Jiri Matas

In 2022, we witnessed another significant improvement in the pose estimation accuracy -- the state of the art, which was 56. 9 AR$_C$ in 2019 (Vidal et al.) and 69. 8 AR$_C$ in 2020 (CosyPose), moved to new heights of 83. 7 AR$_C$ (GDRNPP).

6D Pose Estimation using RGB object-detection +1

A Multi-body Tracking Framework -- From Rigid Objects to Kinematic Structures

1 code implementation2 Aug 2022 Manuel Stoiber, Martin Sundermeyer, Wout Boerdijk, Rudolph Triebel

Our approach focuses on methods that employ Newton-like optimization techniques, which are widely used in object tracking.

3D Object Tracking 6D Pose Estimation +2

Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes

1 code implementation25 Mar 2021 Martin Sundermeyer, Arsalan Mousavian, Rudolph Triebel, Dieter Fox

Our novel grasp representation treats 3D points of the recorded point cloud as potential grasp contacts.

Grasp Generation Robotic Grasping

Unknown Object Segmentation from Stereo Images

2 code implementations11 Mar 2021 Maximilian Durner, Wout Boerdijk, Martin Sundermeyer, Werner Friedl, Zoltan-Csaba Marton, Rudolph Triebel

This has the major advantage that instead of a noisy, and potentially incomplete depth map as an input, on which the segmentation is computed, we use the original image pair to infer the object instances and a dense depth map.

Instance Segmentation Object +2

"What's This?" -- Learning to Segment Unknown Objects from Manipulation Sequences

1 code implementation6 Nov 2020 Wout Boerdijk, Martin Sundermeyer, Maximilian Durner, Rudolph Triebel

Furthermore, while the motion of the manipulator and the object are substantial cues for our algorithm, we present means to robustly deal with distraction objects moving in the background, as well as with completely static scenes.

Foreground Segmentation Object +2

BOP Challenge 2020 on 6D Object Localization

4 code implementations15 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.

6D Pose Estimation 6D Pose Estimation using RGB +4


4 code implementations25 Oct 2019 Maximilian Denninger, Martin Sundermeyer, Dominik Winkelbauer, Youssef Zidan, Dmitry Olefir, Mohamad Elbadrawy, Ahsan Lodhi, Harinandan Katam

BlenderProc is a modular procedural pipeline, which helps in generating real looking images for the training of convolutional neural networks.

3D Object Recognition Depth Image Estimation +3

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