Geometry Encoding Parameter Selection Using Data Signatures
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Solution Overview
Problem
Selecting optimal compression techniques and parameters for geometric data is challenging due to the variety and complexity of available models, making it difficult for users to achieve efficient compression results.
Innovation Solution
A machine learning-based system that selects the best compression technique and parameters for geometric data by using a trained model to determine a unique signature of the data and iteratively testing different options, combining fixed and environmental parameters with machine learning algorithms to optimize encoding and decoding performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the number and variety of compression models increase, then compression quality may improve, but the difficulty of selecting optimal models and parameters increases
Solution Approach 1:
The system automatically selects compression models and parameters by analyzing geometric data properties (vertex valence, angle histograms) and querying a pre-built index of compression models, eliminating the need for user manual selection and reducing selection complexity while maintaining high compression quality
Solution Approach 2:
The system changes the approach from manual parameter selection to automated parameter determination by using machine learning models that map geometric data characteristics to optimal compression parameters, thereby reducing complexity while improving compression quality
2Manufacturing precision
If the number of parameters associated with compression models increases, then compression optimization may improve, but the difficulty of selecting desirable parameters increases
Solution Approach 1:
The system automatically determines optimal parameter values by analyzing geometric data properties and querying the compression model index, eliminating the need for users to manually adjust multiple parameters while achieving optimized compression results
Solution Approach 2:
The system introduces an intermediary layer (machine learning model and index structure) that automatically maps geometric data characteristics to optimal compression parameters, shielding users from parameter selection complexity while maintaining optimization performance
3Productivity
If compression models become more complex, then compression performance may improve, but the computational complexity increases
Solution Approach 1:
The system segments the compression process into two independent phases: a training phase where complex compression models are pre-built and indexed, and an inference phase where simple data analysis and index querying are performed, thereby reducing online computational complexity while maintaining compression performance
Solution Approach 2:
The system performs preliminary action by pre-building and indexing compression models during training, so that during actual compression operations, the system only needs to query pre-computed models rather than perform complex computations in real-time, reducing computational complexity while maintaining performance
Data Source
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AI summary
A method includes receiving geometric data to be encoded, generating a signature for the geometric data based on the at least one property associated with the geometric data, enumerating a first set of options, enumerating a second set of options, encoding the geometric data using the first option and the second option, decoding the encoded geometric data, determining a performance associated with encoding the geometric data, determining a performance associated with decoding the encoded geometric data, and training a regressor based on the signature, the enumerated first option, the enumerated second option, the performance associated with encoding the geometric data and the performance associated with decoding the encoded geometric data.