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 with no code
TransPose: 6D Object Pose Estimation with Geometry-Aware Transformer
To improve robustness to occlusion, we adopt Transformer to perform the exchange of global information, making each local feature contains global information.
ZS6D: Zero-shot 6D Object Pose Estimation using Vision Transformers
The state-of-the-art 6D object pose estimation methods rely on object-specific training and therefore do not generalize to unseen objects.
Exploiting Point-Wise Attention in 6D Object Pose Estimation Based on Bidirectional Prediction
Traditional geometric registration based estimation methods only exploit the CAD model implicitly, which leads to their dependence on observation quality and deficiency to occlusion.
Challenges for Monocular 6D Object Pose Estimation in Robotics
Object pose estimation is a core perception task that enables, for example, object grasping and scene understanding.
YOLOPose V2: Understanding and Improving Transformer-based 6D Pose Estimation
6D object pose estimation is a crucial prerequisite for autonomous robot manipulation applications.
TransPose: A Transformer-based 6D Object Pose Estimation Network with Depth Refinement
In this paper, we propose TransPose, an improved Transformer-based 6D pose estimation with a depth refinement module.
Hierarchical Graph Neural Networks for Proprioceptive 6D Pose Estimation of In-hand Objects
We evaluate our model on a diverse subset of objects from the YCB Object and Model Set, and show that our method substantially outperforms existing state-of-the-art work in accuracy and robustness to occlusion.
Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3)
Addressing pose ambiguity in 6D object pose estimation from single RGB images presents a significant challenge, particularly due to object symmetries or occlusions.
PoseMatcher: One-shot 6D Object Pose Estimation by Deep Feature Matching
However, these methods are often inefficient and limited by their reliance on pre-trained models that have not be designed specifically for pose estimation.
Multi-View Keypoints for Reliable 6D Object Pose Estimation
6D Object pose estimation is a fundamental component in robotics enabling efficient interaction with the environment.
