3D Clustering Navigation for Object Classification
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Solution Overview
Problem
Current methods for classifying three-dimensional (3D) objects in industries such as automotive and aerospace are inefficient due to lack of standardization in naming conventions, reliance on human annotation which is time-consuming and confidential data issues, and difficulty in processing massive similar parts using attribute-based classification and 3D signatures.
Innovation Solution
A computer-implemented method for classifying 3D objects using multi-level clustering, where each object has a signature representative of its morphology, forming a hierarchical tree structure with clusters at different levels, allowing for automatic or user-driven selection and classification of objects based on similarity in morphology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human annotation is used to classify and annotate 3D objects, then classification accuracy can be achieved, but it is time-consuming and cannot handle massive sets of objects
Solution Approach 1:
The system performs preliminary automated classification of 3D objects using shape signatures and clustering algorithms before human validation. This pre-processing step groups similar objects together, so that human annotators only need to validate and correct the clustering results rather than classify each object individually, thereby maintaining accuracy while dramatically improving throughput
Solution Approach 2:
The patent introduces an intermediary automated classification system that acts as a mediator between raw 3D objects and final human-validated classifications. The system uses shape signatures, dimensionality reduction, and clustering algorithms to create initial groupings that humans then validate, effectively distributing the classification workload and improving overall productivity
2Productivity
If attribute-based classification is used to process massive sets of parts, then processing speed improves, but very few attributes are interrelated with the shape making it difficult to process similar parts
Solution Approach 1:
The system transforms the classification parameters from traditional attributes to shape signatures obtained through dimensionality reduction. Instead of using discrete attributes that may not capture shape nuances, the patent converts 3D shapes into continuous signature vectors that preserve geometric information, enabling both speed and accuracy in processing massive sets of similar parts
3Measurement precision
If 3D specifications or signatures are used to describe shape, then comprehensive shape information is captured, but the vectors have huge dimension making component-by-component comparison meaningless
Solution Approach 1:
The patent applies dimensionality reduction techniques to transform high-dimensional shape signature vectors into lower-dimensional representations that preserve the essential geometric information. This allows for meaningful comparison and clustering of 3D objects while reducing computational complexity and avoiding the curse of dimensionality
4Adaptability or versatility
If massive sets of 3D objects are created without standardization, then design flexibility is maintained, but classification becomes inefficient and inconsistent
Solution Approach 1:
The system enables self-service classification where the 3D objects themselves provide the classification information through their shape signatures. The automated clustering algorithm processes objects based on their intrinsic geometric properties without requiring standardized naming conventions or manual metadata, thus maintaining design flexibility while achieving efficient and consistent classification
Data Source
AI summary
A computer-implemented method for classifying three-dimensional (3D) objects including obtaining a set of 3D objects. Each 3D object of the set has a signature representative of the morphology of the 3D object. The method also includes computing a multi-level clustering of the set of 3D objects. The multi-level clustering is a hierarchical tree structure of clusters of 3D objects of the set and has N hierarchical levels. The method also includes selecting, automatically or upon user interaction, one of the computed clusters of a level of the multi-level clustering thereby defining a current level. The method comprises displaying, to a user, 3D objects of the selected cluster in a first part of a display. The method further includes classifying, upon user interaction, the displayed 3D objects. The computer-implemented method improves the classification of 3D objects.