AI 3D Model Generation From Multi-View Image Data
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Solution Overview
Problem
The existing method of creating 3D CAD geometries from 2D sketches is inefficient, requiring multiple sketches per perspective, complex manual adjustments, and involves media discontinuity, leading to cumbersome communication and lengthy design iterations.
Innovation Solution
A method utilizing trained artificial neural networks, specifically generative adversarial networks (GANs), to generate 3D models directly from image data, allowing for targeted interpolation and recombination of image properties to create consistent 2D views from different perspectives, which are then reconstructed into 3D models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If 3D models are created from 2D sketches using CAD software, then design reviews and technical maturity can be achieved, but multiple adjustments per perspective and manual complexity increase
Solution Approach 1:
The patent replaces the mechanical CAD modeling process with an AI-based image generation system. Instead of manually creating 3D models through CAD software requiring multiple sketches and adjustments, the system uses trained artificial neural networks to directly generate consistent 3D models from image data, eliminating the need for repetitive manual adjustments while maintaining manufacturing precision
Solution Approach 2:
The patent creates multiple consistent copies of design perspectives through AI generation. Instead of manually copying and adjusting multiple sketches for different perspectives, the system generates consistent multi-perspective views from a single trained model, reducing the complexity of creating and maintaining multiple design representations
2Productivity
If 3D models are created manually from 2D sketches, then design development can proceed, but media discontinuity and communication complexity increase
Solution Approach 1:
The patent establishes continuity in the design process by using AI models trained on comprehensive design data that capture all necessary information in a unified format. This eliminates media discontinuity where information must be manually transferred between different representation formats (sketches to 3D models), as the AI system maintains continuous information flow throughout the design development process
Solution Approach 2:
The trained AI model serves as an intermediary that understands and processes design information in a unified format. Instead of requiring direct manual translation between 2D sketches and 3D models (which causes information loss), the AI intermediary comprehensively interprets the design intent and generates accurate 3D representations, reducing communication complexity between designers and CAD specialists
3Manufacturing precision
If multiple sketches per perspective are created, then design details can be captured, but adjustment time and iteration cycles increase
Solution Approach 1:
The patent performs preliminary training of AI models on comprehensive design data before actual design generation. This preliminary action embeds all necessary design details and constraints into the trained model, so that during design generation, all details are captured in a single operation rather than requiring multiple iterative adjustments, significantly reducing design iteration time while maintaining precision
Data Source
AI summary
A design device and a method for providing a 3D model for a design development of an object. The method includes specifying an object type to a trained artificial neural network. the artificial neural network is trained with a plurality of basic image data of objects of this object type; generating an image data set of the predetermined object type by the trained artificial neural network, the artificial neural network provides image data from a plurality of perspectives as an image data set; checking the generated image data set for the presence of a design selection criterion, if the design selection criterion is not met, image data are regenerated by a change in an image property criterion; generating a digital 3D model from the image data set.