Search Results for author:
Found 15 papers, 10 papers with code
BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects
In the new tasks, methods were required to learn new objects during a short onboarding stage (max 5 minutes, 1 GPU) from provided 3D object models.
HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation
In this work, we present a novel dense-correspondence method for 6DoF object pose estimation from a single RGB-D image.
6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics
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.
BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects
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).
A Multi-body Tracking Framework -- From Rigid Objects to Kinematic Structures
Our approach focuses on methods that employ Newton-like optimization techniques, which are widely used in object tracking.
Ranked #1 on
3D Object Tracking
on RTB
Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects
Tracking objects in 3D space and predicting their 6DoF pose is an essential task in computer vision.
Ranked #2 on
6D Pose Estimation
on OPT
Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes
Our novel grasp representation treats 3D points of the recorded point cloud as potential grasp contacts.
Unknown Object Segmentation from Stereo Images
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.
"What's This?" -- Learning to Segment Unknown Objects from Manipulation Sequences
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.
BOP Challenge 2020 on 6D Object Localization
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.
Self-Supervised Object-in-Gripper Segmentation from Robotic Motions
Accurate object segmentation is a crucial task in the context of robotic manipulation.
BlenderProc
BlenderProc is a modular procedural pipeline, which helps in generating real looking images for the training of convolutional neural networks.
Multi-path Learning for Object Pose Estimation Across Domains
We introduce a scalable approach for object pose estimation trained on simulated RGB views of multiple 3D models together.
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization.
Ranked #1 on
6D Pose Estimation using RGBD
on T-LESS
