Geometry Encoder ML Compression Selection
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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 existing compression models, making it difficult for users to achieve optimal compression results.
Innovation Solution
A system and method that uses machine learning to select compression techniques and parameters by generating a signature based on geometric data properties, training a regressor, and employing a cost function to determine the best encoding and decoding options, allowing for automatic selection of optimal compression parameters without requiring extensive user knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually select compression models and parameters based on their knowledge, then they can achieve optimal compression results, but the process becomes difficult and time-consuming as the number and variety of compression models increases
Solution Approach 1:
The system performs self-service by automatically selecting compression models and parameters without requiring user intervention. The machine learning model analyzes geometric data properties and autonomously determines the optimal compression configuration, eliminating the need for users to manually navigate through multiple compression models and parameters.
Solution Approach 2:
A machine learning model acts as an intermediary between the user and the compression system. This intermediary receives geometric data, processes it through trained algorithms, and outputs optimal compression parameters, thereby mediating the complex selection process and presenting simplified results to the user.
2Reliability
If the number and variety of compression models increases to provide better compression options, then compression quality improves, but the complexity of selecting desirable models and parameters increases
Solution Approach 1:
The system segments the compression parameter space into distinct categories based on geometric data properties. Different compression models are organized into segments corresponding to specific data characteristics (e.g., mesh density, geometry complexity), allowing the machine learning model to systematically navigate and select from organized options rather than facing a monolithic complex set.
Solution Approach 2:
The machine learning model dynamically changes parameters based on the input geometric data. Instead of requiring users to manually adjust multiple parameters across different models, the system automatically modifies compression parameters according to analyzed properties of the geometric data, such as vertex count, triangle distribution, and geometric complexity metrics.
3Reliability
If users need to understand compression models to select optimal parameters, then compression performance improves, but the learning curve and time required to become proficient increases
Solution Approach 1:
The machine learning model performs preliminary action by pre-processing and analyzing geometric data properties before compression occurs. It extracts features such as mesh density, geometry complexity, and data distribution patterns in advance, using these pre-computed insights to automatically select optimal compression parameters without requiring users to perform any analysis or learning.
Data Source
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.