18491051. METHOD AND APPARATUS FOR 6DOF OBJECT POSE ESTIMATION USING SELF-SUPERVISION LEARNING simplified abstract (ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE)
Contents
- 1 METHOD AND APPARATUS FOR 6DOF OBJECT POSE ESTIMATION USING SELF-SUPERVISION LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND APPARATUS FOR 6DOF OBJECT POSE ESTIMATION USING SELF-SUPERVISION LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Pose Estimation
- 1.13 Original Abstract Submitted
METHOD AND APPARATUS FOR 6DOF OBJECT POSE ESTIMATION USING SELF-SUPERVISION LEARNING
Organization Name
ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE
Inventor(s)
METHOD AND APPARATUS FOR 6DOF OBJECT POSE ESTIMATION USING SELF-SUPERVISION LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18491051 titled 'METHOD AND APPARATUS FOR 6DOF OBJECT POSE ESTIMATION USING SELF-SUPERVISION LEARNING
Simplified Explanation
The patent application describes a method and device for estimating the pose of an object using labeled and unlabeled images.
Key Features and Innovation
- Input labeled source image and unlabeled target image to recognition model for training data generation.
- Train recognition model to generate object information of unlabeled target image.
- Use generated object information as pseudo label for unlabeled target image.
- Train pose estimation model using pseudo-labeled target image and labeled source image.
Potential Applications
This technology can be used in various fields such as robotics, augmented reality, autonomous vehicles, and surveillance systems.
Problems Solved
This technology addresses the challenge of estimating the pose of objects in images without the need for extensive manual labeling.
Benefits
- Improved accuracy in estimating object poses.
- Reduction in manual labeling efforts.
- Enhanced efficiency in object recognition tasks.
Commercial Applications
- Robotics industry for object manipulation.
- Augmented reality applications for accurate object placement.
- Autonomous vehicles for obstacle detection and avoidance.
- Surveillance systems for tracking and monitoring objects.
Prior Art
There are existing methods for object pose estimation, but this patent application introduces a novel approach using pseudo-labeling for training the pose estimation model.
Frequently Updated Research
There may be ongoing research in the field of computer vision and object pose estimation that could impact the development of this technology.
Questions about Pose Estimation
Question 1
How does the use of pseudo-labeling improve the accuracy of object pose estimation?
Question 2
What are the potential limitations of using unlabeled images in training the pose estimation model?
Original Abstract Submitted
Provided are a device and method for estimating a pose of an object. The method includes inputting a labeled source image and an unlabeled target image to a recognition model for generating training data, training the recognition model to generate object information of the unlabeled target image, determining the generated object information to be a pseudo label of the unlabeled target image, and training a pose estimation model for estimating a pose of an object by inputting the pseudo-labeled target image and the labeled source image to the pose estimation model.
