6D Pose Estimation using RGB
86 papers with code • 6 benchmarks • 6 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
LM
ApolloCar3D
Fraunhofer IPA Bin-Picking
Drunkard's Dataset
Latest papers
Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose Estimation
junzastar/dftr_voting
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ICCV 2023
One critical challenge in 6D object pose estimation from a single RGBD image is efficient integration of two different modalities, i. e., color and depth.
Revisiting Fully Convolutional Geometric Features for Object 6D Pose Estimation
jcorsetti/fcgf6d
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Recent works on 6D object pose estimation focus on learning keypoint correspondences between images and object models, and then determine the object pose through RANSAC-based algorithms or by directly regressing the pose with end-to-end optimisations.
SyMFM6D: Symmetry-aware Multi-directional Fusion for Multi-View 6D Object Pose Estimation
Detecting objects and estimating their 6D poses is essential for automated systems to interact safely with the environment.
Shape-Constraint Recurrent Flow for 6D Object Pose Estimation
yanghai-1218/scflow
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CVPR 2023
In this work, we propose a shape-constraint recurrent matching framework for 6D object pose estimation.
EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation
In this paper, we propose the EPro-PnP, a probabilistic PnP layer for general end-to-end pose estimation, which outputs a distribution of pose with differentiable probability density on the SE(3) manifold.
Rigidity-Aware Detection for 6D Object Pose Estimation
yanghai-1218/radet
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CVPR 2023
To address this, we propose a rigidity-aware detection method exploiting the fact that, in 6D pose estimation, the target objects are rigid.
Linear-Covariance Loss for End-to-End Learning of 6D Pose Estimation
Here, we argue that this conflicts with the averaging nature of the PnP problem, leading to gradients that may encourage the network to degrade the accuracy of individual correspondences.
Depth-based 6DoF Object Pose Estimation using Swin Transformer
To tackle this challenge, we propose a novel framework called SwinDePose, that uses only geometric information from depth images to achieve accurate 6D pose estimation.
3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation
deepmind/threednel
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ICCV 2023
In this paper, we introduce probabilistic modeling to the inverse graphics framework to quantify uncertainty and achieve robustness in 6D pose estimation tasks.
Collision-aware In-hand 6D Object Pose Estimation using Multiple Vision-based Tactile Sensors
hsp-iit/multi-tactile-6d-estimation
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The results demonstrate that our approach estimates object poses that are compatible with actual object-sensor contacts in $87. 5\%$ of cases while reaching an average positional error in the order of $2$ centimeters.
