18315597. METHOD AND APPARATUS WITH DEPTH INFORMATION ESTIMATION simplified abstract (Samsung Electronics Co., Ltd.)
Contents
- 1 METHOD AND APPARATUS WITH DEPTH INFORMATION ESTIMATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND APPARATUS WITH DEPTH INFORMATION ESTIMATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
METHOD AND APPARATUS WITH DEPTH INFORMATION ESTIMATION
Organization Name
Inventor(s)
METHOD AND APPARATUS WITH DEPTH INFORMATION ESTIMATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18315597 titled 'METHOD AND APPARATUS WITH DEPTH INFORMATION ESTIMATION
Simplified Explanation
The abstract describes a method of estimating depth information using a simulated image and an artificial neural network model trained on depth maps.
- Method involves generating a simulated image with a first depth map
- Training an artificial neural network model with the first depth map and simulated image
- Generating a second depth map by inputting an actual image into the trained model
- Generating a second simulated image using the second depth map
Potential Applications
This technology could be applied in various fields such as:
- Augmented reality
- Autonomous driving
- Robotics
Problems Solved
This technology addresses the following issues:
- Accurate depth estimation
- Enhancing image processing capabilities
Benefits
The benefits of this technology include:
- Improved depth perception
- Enhanced visual understanding
- Increased accuracy in depth mapping
Potential Commercial Applications
This technology could be commercially utilized in:
- Camera systems
- 3D modeling software
- Medical imaging devices
Possible Prior Art
One possible prior art could be the use of depth maps in computer vision applications for depth estimation.
Unanswered Questions
How does this method compare to traditional depth estimation techniques?
This article does not provide a direct comparison to traditional depth estimation methods. It would be interesting to know the advantages and limitations of this new approach in comparison to existing techniques.
What are the computational requirements for implementing this method?
The article does not delve into the computational resources needed to execute this method. Understanding the computational demands could be crucial for practical applications of this technology.
Original Abstract Submitted
A method of estimating depth information includes generating a first simulated image using a simulator provided with a first depth map, training an artificial neural network model based on the first depth map and the first simulated image, generating a second depth map by inputting an actual image into the trained artificial neural network model, and generating a second simulated image using the simulator provided with the second depth map.
