Autoencoder Neural Network Dimensionality Reduction Procedural Modeling
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Solution Overview
Problem
High-dimensional procedural models in geometry modeling make it difficult and time-consuming for users to generate desired shapes due to the complexity of parameter interactions and the need to adjust multiple parameters simultaneously to achieve specific visual qualities.
Innovation Solution
An autoencoder neural network is trained using both procedural model parameters and visual features from sample objects to reduce the dimensionality of the procedural model, allowing users to generate new objects by adjusting a smaller number of intuitive parameters that output procedural model parameters, thereby simplifying the object generation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a high-dimensional procedural model with many parameters is used to model complex geometry, then the ability to represent diverse object categories and complex phenomena is improved, but the ease of operation and time required to generate desired objects deteriorates
Solution Approach 1:
The patent introduces an autoencoder neural network as an intermediary between the user and the high-dimensional procedural model parameters. The network learns a compressed latent representation that maps user-friendly low-dimensional inputs to the complex parameter space, enabling intuitive control without direct manipulation of numerous parameters
Solution Approach 2:
The patent transforms the high-dimensional parameter space into a lower-dimensional latent space using the autoencoder's bottleneck layer. This dimensionality reduction creates a simplified control interface where users can navigate object generation by adjusting fewer parameters in the compressed space while maintaining access to complex geometry capabilities
2Adaptability or versatility
If a high-dimensional procedural model with many parameters is used to model complex geometry, then the ability to represent diverse object categories and complex phenomena is improved, but the time required to generate desired objects increases
Solution Approach 1:
The patent performs preliminary training of the autoencoder neural network offline to learn the mapping between latent space and procedural parameters. This pre-computed knowledge enables rapid generation during use, as the network can directly predict parameters from compressed representations without requiring iterative optimization or manual adjustment of numerous parameters
Solution Approach 2:
By compressing the high-dimensional parameter space into a lower-dimensional latent representation, the system reduces the computational complexity of parameter adjustment and enables faster navigation through the design space, thereby increasing object generation speed while preserving geometric complexity
Data Source
AI summary
An intuitive object-generation experience is provided by employing an autoencoder neural network to reduce the dimensionality of a procedural model. A set of sample objects are generated using the procedural model. In embodiments, the sample objects may be selected according to visual features such that the sample objects are uniformly distributed in visual appearance. Both procedural model parameters and visual features from the sample objects are used to train an autoencoder neural network, which maps a small number of new parameters to the larger number of procedural model parameters of the original procedural model. A user interface may be provided that allows users to generate new objects by adjusting the new parameters of the trained autoencoder neural network, which outputs procedural model parameters. The output procedural model parameters may be provided to the procedural model to generate the new objects.