6D Pose Estimation
80 papers with code • 5 benchmarks • 12 datasets
Image: Zeng et al
These leaderboards are used to track progress in 6D Pose Estimation
LibrariesUse these libraries to find 6D Pose Estimation models and implementations
Most implemented papers
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
We conduct extensive experiments on our YCB-Video dataset and the OccludedLINEMOD dataset to show that PoseCNN is highly robust to occlusions, can handle symmetric objects, and provide accurate pose estimation using only color images as input.
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
A key technical challenge in performing 6D object pose estimation from RGB-D image is to fully leverage the two complementary data sources.
Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image.
Estimating 6D Pose From Localizing Designated Surface Keypoints
In this paper, we present an accurate yet effective solution for 6D pose estimation from an RGB image.
Segmentation-driven 6D Object Pose Estimation
The most recent trend in estimating the 6D pose of rigid objects has been to train deep networks to either directly regress the pose from the image or to predict the 2D locations of 3D keypoints, from which the pose can be obtained using a PnP algorithm.
Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation
Estimating the 6D pose of objects using only RGB images remains challenging because of problems such as occlusion and symmetries.
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.
PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation
Our method is a natural extension of 2D-keypoint approaches that successfully work on RGB based 6DoF estimation.
CosyPose: Consistent multi-view multi-object 6D pose estimation
Second, we develop a robust method for matching individual 6D object pose hypotheses across different input images in order to jointly estimate camera viewpoints and 6D poses of all objects in a single consistent scene.
FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation
Moreover, at the output representation stage, we designed a simple but effective 3D keypoints selection algorithm considering the texture and geometry information of objects, which simplifies keypoint localization for precise pose estimation.