Triangular Mesh Data Compression Using ML Prediction Residuals
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Solution Overview
Problem
Conventional methods for compressing triangular mesh data are inefficient, particularly when dealing with surfaces that have high curvature or complex local properties, as they do not adequately account for these properties and can be slow and complex to implement.
Innovation Solution
A method involving the definition of a neighborhood of vertices, generation of a neighborhood table, and use of a prediction engine, such as a machine learning engine, to produce predicted primary attribute values, with residuals stored and compressed for efficient transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional compression methods are used for triangular mesh data, then the compression process is simple, but the compression efficiency is poor and handling of high curvature surfaces is inadequate
Solution Approach 1:
The mesh surface is divided into multiple patches, and each patch is processed independently with its own prediction model. This segmentation allows the complex global compression problem to be broken down into manageable local problems, improving both efficiency and accuracy while maintaining reasonable complexity through modular processing
Solution Approach 2:
Prediction models are pre-trained on local patch data before actual compression. By performing preliminary training on small subsets of data, the system prepares optimized prediction parameters in advance, which significantly improves compression efficiency during the actual encoding phase without requiring complex real-time computations
2Measurement precision
If conventional prediction methods are used, then the implementation is fast, but the prediction accuracy is insufficient for complex local properties
Solution Approach 1:
Different prediction models with varying complexities are applied to different local regions of the mesh based on their specific characteristics. High curvature regions use more sophisticated models while flat regions use simpler models, optimizing prediction accuracy where needed without unnecessarily increasing overall system complexity
Solution Approach 2:
The system applies prediction to only the most critical attributes and regions rather than attempting to predict all mesh data uniformly. By focusing computational resources on the most important local properties, the system achieves high prediction accuracy for complex features while keeping the overall approach manageable
3Manufacturing precision
If more data is transmitted to improve reconstruction quality, then the representation accuracy improves, but the transmission time and data volume increase
Solution Approach 1:
Prediction models act as intermediaries between the original mesh data and the compressed representation. These models generate highly accurate predictions from compact residual data, enabling high reconstruction quality to be achieved by transmitting only the essential correction information rather than complete high-precision data
Solution Approach 2:
The system transforms the mesh representation from storing absolute attribute values to storing prediction residuals. This parameter transformation dramatically reduces the amount of data needed for high-quality reconstruction, as residuals require far fewer bits to represent the same level of precision
Data Source
AI summary
Techniques of compressing triangular mesh data involve generating a neighborhood table (i.e., a table) of fixed size that represents a neighborhood of a predicted vertex of a triangle within a triangular mesh for input into a machine-learning (ML) engine. For example, such a neighborhood table as input into a ML engine can output a prediction for a value (e.g., a position) of a vertex. The residual between the prediction and the actual value of the vertex is stored in an array. The data in the array representing the residuals may be compressed and transmitted over a network. Upon receipt by a computer, the array may be decompressed by the computer. Obtaining the actual value involves the receiving computer generating the same neighborhood table, inputting that neighborhood table into the same ML engine to produce the predicted value, and adding the predicted value to the residual from the decompressed file.