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
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.
PoET: Pose Estimation Transformer for Single-View, Multi-Object 6D Pose Estimation
aau-cns/poet
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It is a challenging task due to object symmetries, clutter and occlusion, but it becomes more challenging when additional information, such as depth and 3D models, is not provided.
Video based Object 6D Pose Estimation using Transformers
We introduce a Transformer based 6D Object Pose Estimation framework VideoPose, comprising an end-to-end attention based modelling architecture, that attends to previous frames in order to estimate accurate 6D Object Poses in videos.
CRT-6D: Fast 6D Object Pose Estimation with Cascaded Refinement Transformers
Learning based 6D object pose estimation methods rely on computing large intermediate pose representations and/or iteratively refining an initial estimation with a slow render-compare pipeline.
Self-Supervised Geometric Correspondence for Category-Level 6D Object Pose Estimation in the Wild
While 6D object pose estimation has wide applications across computer vision and robotics, it remains far from being solved due to the lack of annotations.
CASAPose: Class-Adaptive and Semantic-Aware Multi-Object Pose Estimation
Applications in the field of augmented reality or robotics often require joint localisation and 6D pose estimation of multiple objects.
DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation
Establishment of point correspondence between camera and object coordinate systems is a promising way to solve 6D object poses.
Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose Estimators
imitrob/imitrob_dataset_code
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This paper introduces a dataset for training and evaluating methods for 6D pose estimation of hand-held tools in task demonstrations captured by a standard RGB camera.
SC6D: Symmetry-agnostic and Correspondence-free 6D Object Pose Estimation
The pose estimation is decomposed into three sub-tasks: a) object 3D rotation representation learning and matching; b) estimation of the 2D location of the object center; and c) scale-invariant distance estimation (the translation along the z-axis) via classification.
Coupled Iterative Refinement for 6D Multi-Object Pose Estimation
princeton-vl/coupled-iterative-refinement
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CVPR 2022
We propose a new approach to 6D object pose estimation which consists of an end-to-end differentiable architecture that makes use of geometric knowledge.
