Point Cloud Classifier for Dynamic Material Property Classification
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Solution Overview
Problem
Existing methods for classifying and processing point clouds lack precision in distinguishing between different material properties, leading to inefficient processing, editing, and rendering of 3D data points.
Innovation Solution
A point cloud classifier using AI/ML techniques models patterns and relationships between positional and non-positional elements to dynamically attribute material properties to data points, enabling accurate classification and adjustment of processing rules for each set of data points based on specific material properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data points are processed independently, then processing simplicity is maintained, but classification precision deteriorates
Solution Approach 1:
The patent segments data points into distinct groups based on material properties while maintaining independent processing capability for each group. The classification system divides the point cloud into segmented regions with different material characteristics, allowing targeted processing rules to be applied to each segment without requiring complex inter-point dependencies.
Solution Approach 2:
The patent implements dynamic classification where the processing rules are adaptively adjusted based on the identified material properties of data point groups. The system dynamically switches processing parameters and rendering characteristics according to the classified material types, enabling precise control without sacrificing processing simplicity through rigid fixed rules.
2Manufacturing precision
If material property classification is performed, then processing accuracy is improved, but system complexity increases
Solution Approach 1:
The patent uses AI/ML models to create virtual representations and patterns of material properties that can be copied and applied to data point groups. The system learns from training data to generate classification models that replicate material characteristics, enabling accurate processing without requiring complex physical measurement systems for each individual point.
Solution Approach 2:
The patent changes the processing parameters and rendering characteristics based on the classified material properties of data point groups. By adjusting parameters such as processing rules, rendering characteristics, and material property attributes according to the AI classification results, the system achieves high processing accuracy while managing complexity through parameter-based control rather than structural complexity.
3Manufacturing precision
If dynamic classification is applied, then rendering quality is improved, but processing time increases
Solution Approach 1:
The patent performs material property classification as a preliminary action before the main processing and rendering operations. By pre-classifying data point groups into distinct material categories using AI/ML models, the system prepares processing rules and rendering characteristics in advance, enabling efficient batch processing without time-consuming real-time analysis during the main workflow.
Solution Approach 2:
The patent maintains continuous classification and processing action throughout the workflow by applying the same AI-based classification framework consistently across all data point groups. The system continuously identifies material properties and applies appropriate processing rules without interruption, ensuring high rendering quality while minimizing idle time through uninterrupted useful processing action.
Data Source
AI summary
Disclosed is a system for dynamically classifying different data point sets within a point cloud with different classifications that may alter how data point sets with different classifications are processed, edited, and/or rendered. The system may generate a model based on a first set of relationships between a first set of data point elements that result in the first classification, and a second set of relationships between a second set of data point elements that result in the second classification. The system may compare the data point elements from unclassified data point sets against the first set of relationships and the second set of relationships in the model, and may assign the first classification to a particular unclassified data point set in response to the data point elements of the particular data point set having a threshold amount of the first set of relationships.