View source for 18327332. DEVICE AND METHOD WITH SCENE COMPONENT INFORMATION ESTIMATION simplified abstract (Samsung Electronics Co., Ltd.)

=DEVICE AND METHOD WITH SCENE COMPONENT INFORMATION ESTIMATION=


==Organization Name==

[[:Category:Samsung Electronics Co., Ltd.|Samsung Electronics Co., Ltd.]]


[[Category:Samsung Electronics Co., Ltd.]]

==Inventor(s)==

[[:Category:Jinwoo Park of Suwon-si (KR)|Jinwoo Park of Suwon-si (KR)]][[Category:Jinwoo Park of Suwon-si (KR)]]

[[:Category:Nahyup Kang of Suwon-si (KR)|Nahyup Kang of Suwon-si (KR)]][[Category:Nahyup Kang of Suwon-si (KR)]]

[[:Category:Jiyeon Kim of Suwon-si (KR)|Jiyeon Kim of Suwon-si (KR)]][[Category:Jiyeon Kim of Suwon-si (KR)]]

==DEVICE AND METHOD WITH SCENE COMPONENT INFORMATION ESTIMATION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18327332 titled 'DEVICE AND METHOD WITH SCENE COMPONENT INFORMATION ESTIMATION

==Simplified Explanation==

The abstract describes an electronic device that uses an implicit neural representation (INR) model to extract global geometry features and determine object surface positions, albedo information, and specular information from a 2D scene.

* The device uses an INR model to extract global geometry features and determine object surface positions based on information indicating whether a point is on a surface.
* An albedo estimation model is used to estimate albedo information independent of the view direction from the global geometry feature, object surface position, and normal information.
* A specular estimation model is used to estimate specular information dependent on the view direction from the global geometry feature, object surface position, normal information, and view direction.

== Potential Applications ==
This technology could be applied in various fields such as computer vision, augmented reality, virtual reality, and robotics for accurate object recognition and scene understanding.

== Problems Solved ==
This technology solves the problem of accurately estimating object surface positions, albedo information, and specular information from 2D scenes, which is crucial for tasks like object recognition and scene reconstruction.

== Benefits ==
The benefits of this technology include improved accuracy in object recognition, scene understanding, and augmented reality applications, leading to enhanced user experiences and better performance in various tasks.

== Potential Commercial Applications ==
Potential commercial applications of this technology include advanced computer vision systems for autonomous vehicles, robotics, surveillance systems, and augmented reality devices.

== Possible Prior Art ==
Prior art in this field may include research papers, patents, or existing technologies related to object recognition, scene understanding, and computer vision using neural networks and machine learning algorithms.

=== Unanswered Questions ===

=== How does this technology compare to existing methods for object recognition and scene understanding? ===
This article does not provide a direct comparison with existing methods or technologies in the field of object recognition and scene understanding using neural networks and machine learning algorithms.

=== What are the limitations or potential challenges of implementing this technology in real-world applications? ===
The article does not address the potential limitations or challenges of implementing this technology in practical scenarios, such as computational requirements, data processing speed, or accuracy in complex environments.


==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.



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