6D Pose Estimation
96 papers with code • 5 benchmarks • 17 datasets
Image: Zeng et al
Benchmarks
These leaderboards are used to track progress in 6D Pose Estimation
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Libraries
Use these libraries to find 6D Pose Estimation models and implementationsDatasets
YCB-Video
REAL275
HomebrewedDB
ApolloCar3D
NERDS 360
CORSMAL
3D-BSLS-6D
Latest papers
Uncovering the Background-Induced bias in RGB based 6-DoF Object Pose Estimation
We analyze how the presence of the markers affects the pose estimation accuracy, and how this bias may be mitigated through data augmentation and other methods.
BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects
NVlabs/BundleSDF
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CVPR 2023
We present a near real-time method for 6-DoF tracking of an unknown object from a monocular RGBD video sequence, while simultaneously performing neural 3D reconstruction of the object.
IST-Net: Prior-free Category-level Pose Estimation with Implicit Space Transformation
cvmi-lab/ist-net
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ICCV 2023
Category-level 6D pose estimation aims to predict the poses and sizes of unseen objects from a specific category.
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.
Fusing Visual Appearance and Geometry for Multi-modality 6DoF Object Tracking
In this work, we develop a multi-modality tracker that fuses information from visual appearance and geometry to estimate object poses.
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.
Query6DoF: Learning Sparse Queries as Implicit Shape Prior for Category-Level 6DoF Pose Estimation
hustvl/query6dof
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ICCV 2023
Category-level 6DoF object pose estimation intends to estimate the rotation, translation, and size of unseen objects.
Learning Symmetry-Aware Geometry Correspondences for 6D Object Pose Estimation
Taking the symmetry properties of objects into consideration, we design a symmetry-aware matching loss to facilitate the learning of dense point-wise geometry features and improve the performance considerably.
