Neural Network Deformation Basis Inference for 3D Models
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Solution Overview
Problem
Current methods for deforming 3D modeled objects lack efficiency and generalization in computing deformations, requiring target deformations and specific category annotations, and are not optimized for real-time applications.
Innovation Solution
A machine-learning method that learns a neural network to infer a deformation basis for 3D modeled objects, allowing for efficient computation of various deformations without requiring target deformations or category labels, using a dataset of plausible objects from diverse categories, which can be linearly combined for realistic deformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional deformation methods are used, then deformation computation can be performed, but computational efficiency is low and real-time application is not optimized
Solution Approach 1:
The patent pre-computes and stores deformation bases during an offline training phase using a neural network. These pre-computed deformation bases are then reused during real-time applications through linear combinations, eliminating the need for repeated complex computations and enabling efficient real-time deformation.
Solution Approach 2:
The patent replaces traditional mechanical deformation computation methods with a machine learning-based neural network system. The neural network learns deformation patterns from training data and predicts deformation bases, which are then combined linearly to achieve realistic deformations without requiring complex physical simulations.
2Manufacturing precision
If target deformations and category annotations are required, then deformation accuracy can be improved, but system complexity and data requirements increase
Solution Approach 1:
The neural network automatically learns deformation patterns and bases from training data without requiring manual annotation of target deformations or category labels during operation. The system self-adapts to different object categories through the training process, eliminating the need for complex annotation systems during real-time application.
Solution Approach 2:
The patent develops a universal neural network model that can handle multiple object categories and deformation types through a single trained system. The deformation bases learned during training are category-agnostic and can be applied to various objects, reducing the need for category-specific annotations and simplifying the overall system architecture.
3Manufacturing precision
If category-specific training is performed, then deformation accuracy for specific categories improves, but adaptability to new categories decreases
Solution Approach 1:
The neural network is pre-trained on a diverse dataset encompassing multiple categories during an offline phase. This preliminary training equips the network with general deformation knowledge that can be applied to new categories without retraining, achieving both accuracy on training categories and adaptability to unseen categories through linear combination of learned bases.
Data Source
AI summary
A computer-implemented method of machine-learning is described that obtains a dataset of 3D modeled objects. The method further Includes teaching a neural network. The neural network is configured to infer a deformation basis of an input 3D modeled object. This constitutes an improved method of machine-learning.