International business machines corporation (20240161386). GENERATIVE ADVERSARIAL NETWORK BASED IDENTIFICATION OF INDUCED DEFORMATION IN THREE-DIMENSIONAL OBJECT simplified abstract
Contents
- 1 GENERATIVE ADVERSARIAL NETWORK BASED IDENTIFICATION OF INDUCED DEFORMATION IN THREE-DIMENSIONAL OBJECT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GENERATIVE ADVERSARIAL NETWORK BASED IDENTIFICATION OF INDUCED DEFORMATION IN THREE-DIMENSIONAL OBJECT - 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
GENERATIVE ADVERSARIAL NETWORK BASED IDENTIFICATION OF INDUCED DEFORMATION IN THREE-DIMENSIONAL OBJECT
Organization Name
international business machines corporation
Inventor(s)
Tushar Agrawal of West Fargo ND (US)
Martin G. Keen of Cary NC (US)
Sarbajit K. Rakshit of Kolkata (IN)
Jeremy R. Fox of Georgetown TX (US)
GENERATIVE ADVERSARIAL NETWORK BASED IDENTIFICATION OF INDUCED DEFORMATION IN THREE-DIMENSIONAL OBJECT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161386 titled 'GENERATIVE ADVERSARIAL NETWORK BASED IDENTIFICATION OF INDUCED DEFORMATION IN THREE-DIMENSIONAL OBJECT
Simplified Explanation
The abstract describes a method, computer system, and computer program product for identifying induced deformation of a 3D object. The process involves receiving a 3D rendering of an object, identifying influencing factors of forecasted local deformation, creating a 3D rendering showing the forecasted deformation, identifying induced deformation in other portions of the object, and creating a rendering showing the induced deformation.
- Identifying influencing factors of forecasted local deformation based on attribute information of the object
- Creating 3D renderings of the object using a generative adversarial network (GAN)
- Detecting induced deformation in other portions of the object caused by the forecasted deformation
Potential Applications
This technology could be used in industries such as manufacturing, architecture, and animation to predict and visualize deformations in 3D objects.
Problems Solved
This technology helps in identifying and visualizing induced deformations in 3D objects, which can be crucial for quality control, design optimization, and simulation purposes.
Benefits
The benefits of this technology include improved accuracy in predicting deformations, better understanding of object behavior under different conditions, and enhanced visualization capabilities for designers and engineers.
Potential Commercial Applications
Potential commercial applications of this technology include software tools for 3D modeling, simulation, and analysis in various industries such as automotive, aerospace, and entertainment.
Possible Prior Art
One possible prior art could be existing 3D modeling and simulation software that may have similar functionalities for predicting and visualizing deformations in objects.
What are the specific attributes of the object that are used to identify influencing factors of forecasted local deformation?
The abstract mentions using attribute information of the object to identify influencing factors of forecasted local deformation. Specific attributes could include material properties, structural design, dimensions, and surface characteristics.
How does the generative adversarial network (GAN) contribute to creating accurate 3D renderings of the object with forecasted and induced deformations?
The abstract highlights the use of a GAN to create 3D renderings showing forecasted and induced deformations. The GAN likely helps in generating realistic deformations by learning from a dataset of 3D objects and their corresponding deformations, improving the accuracy and visual quality of the renderings.
Original Abstract Submitted
according to one embodiment, a method, computer system, and computer program product for identifying induced deformation of a 3d object is provided. the embodiment may include receiving an unaltered three-dimensional (3d) rendering of an object and attribute information of the object. the embodiment may include identifying one or more influencing factors of forecasted local deformation of one or more portions of the 3d rendering based on the attribute information. the embodiment may include creating, via a generative adversarial network (gan), a 3d rendering of the object showing the forecasted local deformation. the embodiment may include identifying induced deformation of one or more other portions of the 3d rendering caused by the forecasted local deformation. the embodiment may include creating, via the gan, a 3d rendering of the object showing the identified induced deformation.
