Roof Modeling From 2D Imagery Using Partial 3D Data
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Solution Overview
Problem
Existing software systems struggle to accurately depict elevation and detect internal line segments in 3D models generated from 2D images, leading to inaccurate or incomplete 3D representations of structures.
Innovation Solution
A computer vision system that processes two-dimensional and partial three-dimensional data to reconstruct 3D structures by performing imagery selection, neural network inference, line extraction, line graph construction, and 3D reconstruction, using techniques such as line segment geometries, gradient assignments, and straight skeleton algorithms to generate accurate 3D models of structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing software systems process 2D images to generate 3D models, then the modeling process can be automated, but the accuracy of elevation depiction and internal line segment detection deteriorates
Solution Approach 1:
The patent segments the 3D modeling process into distinct stages: 2D image processing, line segment extraction, line graph construction, and 3D reconstruction. Each stage handles specific tasks independently, with line segments extracted from 2D images being processed separately from elevation data, allowing each component to be optimized for its specific function while maintaining overall automation.
Solution Approach 2:
The patent introduces an intermediary line graph structure that mediates between 2D image data and final 3D model generation. This line graph serves as a intermediate representation that incorporates both 2D line segment information and 3D elevation data, resolving the contradiction by allowing accurate elevation depiction while maintaining automation through structured data processing.
2Extent of automation
If existing software systems process 2D images to generate 3D models, then the modeling process can be automated, but the completeness of internal line segment detection deteriorates
Solution Approach 1:
The patent segments line detection into multiple passes: initial line segment extraction from 2D images, followed by a line graph construction phase where internal line segments are identified and connected. This multi-stage segmentation allows the system to detect both external and internal line segments systematically, improving completeness while maintaining automation.
Solution Approach 2:
The patent implements feedback mechanisms where the line graph construction process refines and validates detected line segments against the original 2D image data and 3D elevation information. This feedback loop ensures that internal line segments are properly detected and connected, improving reliability while preserving automated processing.
3Device complexity
If 3D models are generated from 2D images without internal line segments, then the processing is simpler, but the model becomes inaccurate and incomplete
Solution Approach 1:
The patent segments the processing into distinct phases where line segment extraction, line graph construction, and 3D reconstruction are handled separately. This segmentation allows the system to process 2D images simply in the initial phase, then systematically add internal line segments and elevation data in subsequent phases, achieving accuracy without overwhelming complexity.
Solution Approach 2:
The patent performs preliminary line segment extraction from 2D images before incorporating 3D elevation data. This preliminary action establishes a foundation of detected line segments that can then be enhanced with 3D information, allowing the system to build accurate 3D models by adding complexity incrementally rather than processing everything simultaneously.
Data Source
AI summary
A system for modeling a roof of a structure comprising a first database, a second database and a processor in communication with the first database and the second database. The processor selects one or more images and the respective metadata thereof from the first database based on a received a geospatial region of interest. The processor generates two-dimensional line segment geometries in pixel space based on two-dimensional outputs generated by a neural network in pixel space of at least one roof structure present in the selected one or more images. The processor classifies the generated two-dimensional line segment geometries into at least one contour graph based on three-dimensional data received from the second database and generates a three-dimensional representation of the at least one roof structure based on the at least one contour graph and the received three-dimensional data.