Automated 2D/3D Structure Wireframes Using Geometric Primitives
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Solution Overview
Problem
Current methodologies fail to generate accurate wireframe renderings automatically from 3D data, particularly for complex or non-standard structures, requiring manual intervention and validation, and lack precise measurements and geometric information.
Innovation Solution
The method involves processing 2D and 3D data to extract geometric primitives from structures, using machine learning and computer vision techniques to generate wireframe renderings directly from 2D images, and incorporating these into a machine learning training set for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic algorithms are used to generate wireframe renderings from 3D data, then productivity is improved, but manufacturing precision deteriorates
Solution Approach 1:
The algorithm segments the 3D point cloud data into distinct geometric primitives (planes, cylinders, cones, spheres) by analyzing local geometric patterns and curvature characteristics. This segmentation enables automatic identification of structural elements while maintaining precision through primitive-specific fitting algorithms.
Solution Approach 2:
The system introduces geometric primitives as intermediary representations between raw 3D data and final wireframe renderings. These primitives serve as mediators that bridge the gap between automated processing and precise geometric representation, allowing accurate modeling of complex structures through composition of basic geometric forms.
2Ease of operation
If existing algorithms are used to extract wireframe renderings from 3D data, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The algorithm dynamically adjusts fitting parameters and geometric constraints based on the detected primitive type and local data characteristics. By changing parameters adaptively during processing, the system maintains high measurement precision for diverse geometric forms while preserving automatic operation ease.
Solution Approach 2:
The system transitions from 3D point cloud data to 2D wireframe representations through dimensionality reduction, while preserving critical geometric information. This dimensional transformation enables accurate measurement extraction by projecting 3D geometric relationships onto 2D planes where precision can be maintained through careful parameter selection.
3Manufacturing precision
If manual evaluation and manipulation are performed to resolve geometric features, then manufacturing precision is improved, but productivity deteriorates
Solution Approach 1:
The algorithm performs self-validation and automatic correction of geometric features by checking consistency across multiple primitives and iteratively refining fits. This self-service capability eliminates the need for manual evaluation while maintaining high precision through built-in quality control mechanisms.
Solution Approach 2:
The system implements feedback loops where detected geometric primitives are used to guide subsequent detection and fitting processes. Measurements from identified primitives provide feedback that refines parameter estimation and improves accuracy of adjacent features, enabling automatic high-precision processing without manual intervention.
Data Source
AI summary
Examples relate generally to improvements in generation of wireframe renderings derived from 2D and/or 3D data that includes at least one structure of interest in a scene. Such wireframe renderings and similar formats can be used in, among other things, 2D/3D CAD drawings, designs, drafts, models, building information models, augmented reality or virtual reality, and the like. Measurements, dimensions, geometric information, and semantic information generated according to the inventive methods can be accurate in relation to the actual structures. The wireframe renderings can be generated from a combination of a plurality of 2D images and point clouds, processing of point clouds to generate virtual/synthetic views to be used with the point clouds, or from 2D image data that has been processed in a machine learning process to generate 3D data. In some aspects, the wireframe renderings are accurate in relation to the actual structure of interest, automatically generated, or both.