Samsung electronics co., ltd. (20240177408). DEVICE AND METHOD WITH SCENE COMPONENT INFORMATION ESTIMATION simplified abstract
Contents
- 1 DEVICE AND METHOD WITH SCENE COMPONENT INFORMATION ESTIMATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DEVICE AND METHOD WITH SCENE COMPONENT 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
DEVICE AND METHOD WITH SCENE COMPONENT INFORMATION ESTIMATION
Organization Name
Inventor(s)
DEVICE AND METHOD WITH SCENE COMPONENT INFORMATION ESTIMATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240177408 titled 'DEVICE AND METHOD WITH SCENE COMPONENT INFORMATION ESTIMATION
Simplified Explanation
The abstract describes an electronic device that uses neural representation models to extract global geometry features and determine object surface positions, albedo information, and specular information from image pixels corresponding to a 2D scene.
- The device extracts global geometry features and surface information from image pixels using neural representation models.
- It determines object surface positions and normal information based on the extracted data.
- It estimates albedo information independent of the view direction and specular information dependent on the view direction using specialized models.
Potential Applications
This technology could be applied in various fields such as computer vision, augmented reality, virtual reality, and robotics for accurate object recognition, scene understanding, and rendering realistic graphics.
Problems Solved
This technology solves the problem of accurately estimating object surface properties and positions from 2D images, which is crucial for applications like object recognition and scene reconstruction.
Benefits
The benefits of this technology include improved accuracy in object surface estimation, better rendering of realistic graphics, and enhanced performance in computer vision tasks.
Potential Commercial Applications
Potential commercial applications of this technology include advanced image processing software, augmented reality applications, virtual reality systems, and robotics for industrial automation.
Possible Prior Art
One possible prior art for this technology could be research papers or patents related to neural representation models for object recognition and scene understanding in computer vision applications.
Unanswered Questions
How does this technology compare to existing methods for object surface estimation from images?
This technology offers a more accurate and efficient way to estimate object surface properties from 2D images compared to traditional methods that rely solely on image processing techniques.
What are the limitations of this technology in real-world applications?
One limitation of this technology could be its computational complexity, which may affect its real-time performance in certain applications. Additionally, the accuracy of the estimations may vary depending on the complexity of the scene and the quality of the input images.
Original Abstract Submitted
an electronic device includes: one or more processors configured to: extract, using an implicit neural representation (inr) model, a global geometry feature and information indicating whether a point is on a surface from a viewpoint and a view direction corresponding to an image pixel corresponding to a two-dimensional (2d) scene at the viewpoint within a field of view (fov); determine an object surface position corresponding to the viewpoint and the view direction and normal information of the object surface position based on the information indicating whether the point is on the surface; estimate, using an albedo estimation model, albedo information independent of the view direction from the global geometry feature, the object surface position, and the normal information; and estimate, using a specular estimation model, specular information dependent on the view direction from the global geometry feature, the object surface position, the normal information, and the view direction.
