View source for ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE (20240282049). METHOD AND APPARATUS FOR RECONSTRUCTING A THREE-DIMENSIONAL SHAPE BASED ON MULTIPLE LIGHT SOURCES simplified abstract

=METHOD AND APPARATUS FOR RECONSTRUCTING A THREE-DIMENSIONAL SHAPE BASED ON MULTIPLE LIGHT SOURCES=


==Organization Name==

[[:Category:ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE|ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE]]


[[Category:ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE]]

==Inventor(s)==

[[:Category:Joon Soo Kim of Daejeon (KR)|Joon Soo Kim of Daejeon (KR)]][[Category:Joon Soo Kim of Daejeon (KR)]]

==METHOD AND APPARATUS FOR RECONSTRUCTING A THREE-DIMENSIONAL SHAPE BASED ON MULTIPLE LIGHT SOURCES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240282049 titled 'METHOD AND APPARATUS FOR RECONSTRUCTING A THREE-DIMENSIONAL SHAPE BASED ON MULTIPLE LIGHT SOURCES

'''Simplified Explanation:'''
The patent application describes a device, method, and system for reconstructing a 3D shape using multiple light sources. The method involves capturing images of an object under various lighting conditions, creating a 3D grid, calculating scattering potential values, and determining the refractive index distribution through an artificial neural network model.

* Obtaining measured images of a specimen under different lighting conditions
* Defining a 3D grid within a reconstruction area
* Calculating scattering potential values within the grid
* Determining the refractive index distribution using an artificial neural network model
* Training the neural network model based on the differences between predicted and measured images

'''Key Features and Innovation:'''
- Utilization of multiple light sources for 3D shape restoration
- Incorporation of artificial neural network model for refractive index determination
- Training the model based on image differences for accuracy

'''Potential Applications:'''
- Medical imaging for accurate diagnosis
- Industrial quality control for precise measurements
- Archaeological research for artifact reconstruction

'''Problems Solved:'''
- Inaccuracies in 3D shape restoration
- Difficulty in determining refractive index distribution
- Lack of precision in scattering potential calculations

'''Benefits:'''
- Enhanced accuracy in 3D shape reconstruction
- Improved efficiency in refractive index determination
- Increased reliability in scattering potential calculations

'''Commercial Applications:'''
Title: Advanced 3D Shape Restoration System with Multiple Light Sources
This technology can be applied in industries such as healthcare, manufacturing, and research for high-precision imaging and analysis. It has the potential to revolutionize the way 3D shapes are reconstructed and refractive indices are determined.

'''Prior Art:'''
Prior research in the field of 3D shape reconstruction using neural networks and light sources can provide valuable insights into similar technologies.

'''Frequently Updated Research:'''
Stay updated on advancements in artificial neural networks, 3D imaging technologies, and light source applications for shape reconstruction to enhance the efficiency and accuracy of this system.

'''Questions about 3D Shape Restoration with Multiple Light Sources:'''
1. How does the use of multiple light sources improve the accuracy of 3D shape restoration?
2. What are the key challenges in training the artificial neural network model for refractive index determination?


==Original Abstract Submitted==

a device, method, and system for restoring a 3d shape based on multiple light sources are disclosed. a method performed by a device may include obtaining a set of measured images of a specimen by photographing the specimen under different lighting conditions; defining a first three-dimensional grid within a 3d reconstruction area including all or part of the specimen; obtaining a first set of coordinate values for calculating a scattering potential value within a first 3d grid or a frequency grid region corresponding to the first 3d grid; and obtaining a 3d refractive index distribution of the specimen based on the output data obtained through an artificial neural network model, and the artificial neural network model is trained based on differences between the set of predicted images and the set of the measured images.



[[Category:G06T15/50]]
[[Category:G06T7/11]]
[[Category:G06T7/73]]
[[Category:CPC_G06T15/506]]