3D Object Design via Latent Vector Optimization
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Solution Overview
Problem
There is a need for an improved method for designing 3D modeled objects via user-interaction that leverages machine-learning techniques effectively.
Innovation Solution
The method utilizes a machine-learnt decoder to design 3D modeled objects by determining an optimal latent vector that minimizes an energy function penalizing non-respect of user-defined deformation constraints, thereby outputting a 3D modeled object that respects the user's design intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional CAD systems are used for designing 3D modeled objects, then design flexibility is maintained, but user interaction complexity and time consumption increase
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the user and the complex CAD system. The user provides simple deformation constraints, and the ML model automatically generates the optimal 3D object configuration, eliminating the need for users to navigate complex CAD interfaces and perform manual modeling operations.
Solution Approach 2:
The system enables self-service by allowing the 3D design system to automatically optimize object configurations based on user constraints. The machine learning model independently performs the complex design optimization tasks without requiring continuous user intervention or adjustment of multiple parameters.
2Extent of automation
If machine learning techniques are integrated into 3D design systems, then design automation improves, but system complexity increases
Solution Approach 1:
The patent uses a pre-trained machine learning model that has been trained on a large dataset of 3D objects. This pre-trained model can be directly applied to new design tasks without requiring retraining, simplifying the system architecture and reducing the complexity of integrating ML capabilities into the CAD system.
3Manufacturing precision
If user-defined deformation constraints are applied, then design precision improves, but computational requirements increase
Solution Approach 1:
The machine learning model is pre-trained on a comprehensive dataset of 3D objects and deformation patterns. This preliminary training enables the model to quickly infer optimal configurations from user constraints without requiring computationally intensive real-time calculations, thus reducing energy consumption during actual design operations.
Data Source
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AI summary
The invention notably relates to a computer-implemented method for designing a 3D modeled object via user-interaction. The method comprises providing the 3D modeled object and a machine-learnt decoder. The machine-learnt decoder is a differentiable function taking values in a latent space and outputting values in a 3D modeled object space. The method further comprises defining, by a user, a deformation constraint for a part of the 3D modeled object. The method further comprises determining an optimal vector. The optimal vector minimizes an energy. The energy explores latent vectors. The energy comprises a term which penalizes, for each explored latent vector, non-respect of the deformation constraint by the result of applying the decoder to the explored latent vector. The method further comprises applying the decoder to the optimal latent vector. This constitutes an improved method for designing a 3D modeled object via user-interaction.