3D Model Compression via Geometric Function Fitting
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Solution Overview
Problem
The transfer of large 3D models over data networks is inefficient due to their significant size, causing delays and impracticality in collaborative design and development, as existing methods do not effectively compress the data without compromising the accuracy and visualization of the models.
Innovation Solution
A compression system that uses AI/ML techniques to identify and replace sets of constructs in 3D models with functions, noise patterns, and gradient patterns, allowing for a compressed representation that recreates the shapes and coloring with high accuracy, thereby reducing the file size and transfer time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If 3D model data is transferred over data networks, then the models can be shared and collaborated on, but the transfer time becomes excessively long due to large file sizes
Solution Approach 1:
The patent extracts only the essential geometric information needed to represent 3D shapes by fitting mathematical functions (planes, spheres, cylinders, cones) to point cloud data. This removes redundant data while preserving the core shape characteristics, enabling fast transmission without sacrificing collaborative utility
Solution Approach 2:
The patent transforms the representation parameters of 3D data from storing individual point coordinates to storing mathematical function parameters (coefficients, radii, heights). This parameter transformation dramatically reduces data size while maintaining shape accuracy, resolving the transfer time bottleneck
2Loss of time
If 3D model data is compressed to reduce file size, then transfer time decreases, but the accuracy and visualization quality may be compromised
Solution Approach 1:
The patent replaces the mechanical storage approach (storing individual point coordinates) with a mathematical representation system (fitting geometric functions to points). This substitution maintains high shape accuracy while achieving significant compression, as mathematical functions describe complex shapes with far fewer parameters than raw point data
Solution Approach 2:
The patent uses a composite representation combining multiple geometric primitives (planes, spheres, cylinders, cones) to accurately model complex 3D shapes. This composite approach maintains visualization quality by building accurate shape representations from simpler mathematical components
3Reliability
If large amounts of 3D data are stored and transferred, then complete model fidelity is maintained, but network bandwidth consumption increases and collaboration becomes impractical
Solution Approach 1:
The patent creates a simplified mathematical copy of the 3D model that captures essential shape characteristics. This copy uses fitted geometric functions to represent the original point cloud, maintaining sufficient fidelity for collaboration while reducing data size to practical transmission levels
Solution Approach 2:
The patent segments the 3D space into multiple octants and processes point clouds in each segment independently. This segmentation allows efficient local fitting of geometric functions to specific regions, maintaining overall model fidelity while enabling manageable data processing and transmission
Data Source
AI summary
Disclosed is a system and associated methods for compressing data in a three-dimensional (“3D”) model. The system receives the constructs that form different shapes of a 3D object represented by the 3D model. The system selects a set of the constructs based on the set of constructs forming a particular shape that is compressible with a function. The system defines the function that generates an approximate shape for the particular shape formed by the set of constructs, and compresses the 3D model by replacing the set of constructs with the function. The system may tune the function so that the approximate shape matches the particular shape with more specificity, may define a noise pattern that approximates and applies the non-uniformity of the particular shape to the approximate shape, and may define a gradient pattern that approximates and applies the coloring of the set of constructs to the approximate shape.