Structure Self-Adaptive 3D Model Editing via Clustering
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Solution Overview
Problem
The automation of 3D model editing is hindered by the vast differences in 3D model structures and sizes, making it challenging to develop an efficient and unified editing process.
Innovation Solution
A structure self-adaptive 3D model editing method that clusters models by structure, learns intra-group and inter-group design knowledge priors using multivariate linear regression, and optimizes user-edited models through interactive tools to ensure structural rationality and increased automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a unified 3D model editing procedure is applied to all models, then the editing process is simplified, but the editing accuracy and adaptability to different model structures deteriorate
Solution Approach 1:
The patent segments the unified editing procedure into structure-specific sub-procedures by clustering models into different structure groups. Each group receives tailored editing guidance based on its structural characteristics, thus maintaining both procedural simplicity and editing accuracy for diverse model types.
Solution Approach 2:
The patent implements dynamic adaptation of the editing procedure by automatically identifying the structure group of each model and applying corresponding design knowledge priors. This allows the editing system to dynamically adjust its behavior to match the specific structural features of each model, resolving the contradiction between unified process and precise adaptation.
2Manufacturing precision
If structure-specific editing procedures are developed for each model type, then the editing accuracy improves, but the device complexity and procedure diversity increase
Solution Approach 1:
The patent creates a universal editing framework that handles multiple model types through a single system. The framework automatically identifies model structure groups and applies appropriate editing strategies, eliminating the need for separate editing procedures for each model type while maintaining high editing accuracy across diverse structures.
Solution Approach 2:
The editing system performs self-adaptation by automatically clustering models into structure groups and selecting appropriate design knowledge priors without user intervention. This self-service mechanism reduces the complexity of managing multiple editing procedures while maintaining structure-specific editing accuracy.
3Manufacturing precision
If manual structure identification is performed for each model, then the editing precision is maintained, but the productivity and automation level decrease
Solution Approach 1:
The patent performs preliminary clustering of models into structure groups before the actual editing process. By pre-organizing models according to their structural characteristics and pre-computing design knowledge priors for each group, the system eliminates the need for manual structure identification during editing, thus maintaining accuracy while significantly improving productivity and automation.
Solution Approach 2:
The patent replaces manual structure identification (mechanical process) with automated algorithmic clustering and classification. This substitution uses computational methods to automatically identify model structures and assign them to appropriate groups, maintaining identification accuracy while dramatically increasing editing speed and automation level.
4Adaptability or versatility
If design knowledge priors are learned for all model structures, then the adaptability improves, but the loss of time for learning and processing increases
Solution Approach 1:
The patent segments the design knowledge priors into structure-group-specific subsets rather than learning all priors for all model structures. By clustering models into distinct structure groups and learning priors only for each group's characteristic structures, the system achieves broad adaptability across diverse models while reducing the time required for learning and processing by focusing on relevant knowledge subsets.
Data Source
AI summary
The invention provides a structure self-adaptive 3D model editing method, which includes: given a 3D model library, clustering 3D models of same category according to structures; learning a design knowledge prior between components of 3D models in same group; learning a structure switching rule between 3D models in different groups; after user edits a 3D model component, determining a final group of the model according to inter-group design knowledge prior, and editing other components of the model according to intra-group design knowledge prior, so that the model as a whole satisfies design knowledge priors of a category of 3D models. Through editing few components by the user, other components of the model can be optimized automatically and the edited 3D model satisfying prior designs of the model library can be obtained. The invention can be applied to fields of 3D model editing and constructing, computer aided design etc.