6D Pose Estimation using RGB
78 papers with code • 6 benchmarks • 5 datasets
6D Pose Estimation using RGB refers to the task of determining the six degree-of-freedom (6D) pose of an object in 3D space based on RGB images. This involves estimating the position and orientation of an object in a scene, and is a fundamental problem in computer vision and robotics. In this task, the goal is to estimate the 6D pose of an object given an RGB image of the object and the scene, which can be used for tasks such as robotic manipulation, augmented reality, and scene reconstruction.
( Image credit: Segmentation-driven 6D Object Pose Estimation )
Benchmarks
These leaderboards are used to track progress in 6D Pose Estimation using RGB
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Libraries
Use these libraries to find 6D Pose Estimation using RGB models and implementations
YCB-Video
T-LESS
ApolloCar3D
Fraunhofer IPA Bin-Picking
Drunkard's Dataset
Most implemented papers
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
yuxng/PoseCNN
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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.
Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
hughw19/NOCS_CVPR2019
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CVPR 2019
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
sjtuytc/betapose
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In this paper, we present an accurate yet effective solution for 6D pose estimation from an RGB image.
Real-Time Seamless Single Shot 6D Object Pose Prediction
Microsoft/singleshotpose
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CVPR 2018
For single object and multiple object pose estimation on the LINEMOD and OCCLUSION datasets, our approach substantially outperforms other recent CNN-based approaches when they are all used without post-processing.
Segmentation-driven 6D Object Pose Estimation
cvlab-epfl/segmentation-driven-pose
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CVPR 2019
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
kirumang/Pix2Pose
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ICCV 2019
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.
PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation
mialbro/PointFusion
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CVPR 2018
We present PointFusion, a generic 3D object detection method that leverages both image and 3D point cloud information.
PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation
We further create a Truncation LINEMOD dataset to validate the robustness of our approach against truncation.
HybridPose: 6D Object Pose Estimation under Hybrid Representations
chensong1995/HybridPose
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CVPR 2020
Compared to a unitary representation, our hybrid representation allows pose regression to exploit more and diverse features when one type of predicted representation is inaccurate (e. g., because of occlusion).
