3D Wireframe Model Generation via Adaptive Part Segmentation
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Solution Overview
Problem
Current methods for generating 3D wireframe models from volumetric scans result in large data files due to high resolution, making them cumbersome and resource-intensive, especially in applications like computer games and video animation where fast rendering and limited storage are required, and fail to adapt quality representation to different parts of complex objects effectively.
Innovation Solution
A computer-implemented method that uses volumetric scanning to generate voxel maps, segments the object into parts, classifies these parts using user-defined classes, and adapts the quality of 3D wireframe models based on user-selected parameters, employing self-learning modules like artificial neural networks to optimize polygon count, texture, and color application for each class, allowing for dynamic modeling and realistic simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high resolution voxel maps are used to represent the entire volume of the object, then measurement precision is improved, but device complexity and data storage requirements increase significantly
Solution Approach 1:
The patent segments the object into multiple parts and creates separate wireframe models for each part. This segmentation allows the system to avoid generating high-resolution voxel maps for the entire object, instead creating focused wireframe representations only where needed, thereby reducing overall data complexity while maintaining precision for critical regions.
Solution Approach 2:
The patent applies different quality levels to different parts of the object based on their importance. Critical parts receive higher resolution wireframe models while less important parts use lower resolution representations. This local quality approach maintains measurement precision for essential components while significantly reducing the total data file size.
2Manufacturing precision
If high resolution wireframe models are generated for all parts, then manufacturing precision is improved, but productivity decreases due to increased processing time and resource requirements
Solution Approach 1:
The system identifies critical parts that require high manufacturing precision and applies high-resolution wireframe modeling only to those specific components. Non-critical parts receive lower resolution models, thereby maintaining accuracy where needed while significantly improving overall processing speed and productivity.
Solution Approach 2:
Instead of applying full high-resolution modeling to all parts, the system applies partial action by focusing computational resources only on critical parts that require high precision. This selective approach maintains manufacturing precision for essential components while reducing total processing time and resource consumption.
3Manufacturing precision
If uniform quality is applied to all parts of the object, then manufacturing precision is maintained, but adaptability decreases as different parts have different quality requirements
Solution Approach 1:
The patent implements local quality by assigning different quality parameters to different parts based on their specific requirements. Critical parts receive high-resolution wireframe models with detailed geometry, while less important parts use coarser representations. This approach maintains manufacturing precision for essential components while providing adaptability to accommodate varying quality needs across different parts.
Solution Approach 2:
The system dynamically adjusts the quality of wireframe models for different parts based on their importance and usage requirements. This dynamic quality adaptation allows the system to optimize the balance between manufacturing precision and resource utilization, making the modeling process versatile and adaptable to different scenarios.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces data size while maintaining high-quality representation for critical parts, enhances rendering and texture quality, and optimizes processing efficiency by adapting model quality to specific components, resulting in faster manipulation and storage of 3D models.
Implementation Method 1
Scanning the object with a computer tomography method, so as to generate a voxel map
Data Source
AI summary
A computer-implemented method (100) for generating a 3-dimensional wireframe model (13) of an object (2) comprising a plurality of parts, comprising: Scanning (10) the object (2), preferably with a computer tomography method (10a), so as to generate a voxel map; —Computing (12) a 3 dimensional wireframe model (13) of the object; —Segmenting (14) into a plurality of 3 dimensional wireframe part models (15), each 3 dimensional part model corresponding to one part of the object; Using a self-learning machine for classifying (16) said parts into part classes (17); —Adapting (18) the quality of the 3 dimensional wireframe part models depending on at least one quality parameter selected by the user (70) independently for at least one/one or more class (c1, c2, . . . cn). A texture may be applied to the model by photogrammetry and/or PBR.