Automated 3D Structure Modeling from Digital Imagery
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Solution Overview
Problem
Current methods for extracting 3D wireframes from imagery require significant human interaction and are inefficient, lacking automation and scalability, especially in determining structural features like roof pitch and eave height from digital images.
Innovation Solution
An automated system and method that uses convolutional neural networks and computer vision techniques to generate heat maps and three-dimensional models from digital images, allowing for the extraction of structural feature information and measurements without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual photogrammetry methods are used to extract structural features, then measurement accuracy can be achieved, but significant human interaction and time are required
Solution Approach 1:
The patent replaces manual mechanical measurement processes with automated computer vision algorithms. Specifically, neural networks and image processing systems automatically identify and measure structural features (roof pitch, eave height) from images, eliminating the need for manual field measurements and significantly reducing processing time while maintaining accuracy
Solution Approach 2:
The system enables self-service by allowing the images themselves to provide the measurement data through automated analysis. The structural features extract information directly from the image data without requiring human intervention in the measurement process, making the system autonomous and efficient
2Manufacturing precision
If traditional modeling software is used to create 3D wireframes, then detailed structural models can be generated, but significant human interaction and lack of automation are required
Solution Approach 1:
The patent replaces traditional interactive CAD modeling with automated 3D reconstruction algorithms. The system automatically generates accurate 3D wireframe models by processing images through computer vision pipelines, eliminating manual modeling steps while preserving geometric precision through mathematical projection and coordinate transformation
Solution Approach 2:
The system creates accurate digital copies of physical structures by extracting geometric information from images. The automated process reproduces the 3D structure's wireframe representation with high fidelity, capturing essential geometric features without requiring manual recreation of each element
3Extent of automation
If dense point clouds are used for roof pitch determination, then automated processing is achieved, but poor accuracy and limited scalability result
Solution Approach 1:
The patent extracts only the essential geometric features needed for measurement (roof edges, ridges, eaves) directly from images using targeted detection algorithms. This selective extraction approach avoids the computational burden and accuracy issues of dense point clouds while maintaining automation, focusing resources on critical measurement points rather than processing all pixels
Data Source
AI summary
Methods and systems for automated structure modeling form digital imagery are disclosed, including a method comprising receiving target digital images depicting a target structure; automatically identifying target elements of the target structure in the target digital images using convolutional neural network semantic segmentation; automatically generating a heat map model depicting a likelihood of a location of the target elements of the target structure; automatically generating a two-dimensional model or a three-dimensional model of the target structure based on the heat map model without further utilizing the target digital images; and extracting information regarding the target elements from the two-dimensional or the three-dimensional model of the target structure.