3D Structure Modeling Through Geometry Extraction and Data Fusion
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Solution Overview
Problem
Existing systems struggle to generate accurate three-dimensional models of structures, particularly buildings, due to the lack of efficient data processing and feature extraction techniques, especially for interior and exterior features, which are crucial for industries like insurance underwriting, building construction, and real estate.
Innovation Solution
A system and method utilizing geometry extraction and reconstruction techniques, employing a structure modeling engine on computing devices to process raw data from sensors, including photos, LIDAR, and GPS, with data fusion and feature extraction to reconstruct large- and small-scale structural features, and identify damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing methods are used to generate 3D models of structures, then the system complexity remains low, but the accuracy and detail of the generated models deteriorates
Solution Approach 1:
The system segments the 3D modeling process into distinct modules: data acquisition from multiple sensors, data fusion processing, geometry extraction, and model reconstruction. Each module handles specific tasks independently, improving overall model accuracy while managing system complexity through functional decomposition
Solution Approach 2:
The system integrates multiple sensor types (cameras, LIDAR, GPS, accelerometers) into a unified data processing framework that can handle various data formats and sensor combinations. This multi-functional approach enables accurate 3D modeling across different structure types and environments without requiring separate specialized systems
2Loss of information
If comprehensive sensor data is collected from multiple sources, then the detail and completeness of structural features improves, but the data processing time and computational resources increases
Solution Approach 1:
The system performs preliminary data fusion and preprocessing of sensor data before the main 3D reconstruction process. By organizing and validating data from multiple sensors in advance, the system reduces computational burden during model generation while preserving complete structural feature information
Solution Approach 2:
The system creates intermediate data representations and fused data models that serve as simplified copies of the raw sensor data. These intermediate representations maintain essential geometric and spatial information while reducing data volume for faster processing in subsequent reconstruction stages
3Adaptability or versatility
If geometry extraction and reconstruction techniques are implemented, then the capability to identify specific structural features and damage improves, but the algorithm complexity and computational requirements increases
Solution Approach 1:
The system introduces geometry extraction algorithms as intermediary processing steps between raw sensor data and final 3D reconstruction. These extraction algorithms identify and isolate specific structural features (walls, floors, ceilings, damage areas) to guide the reconstruction process, enhancing feature identification capability while managing algorithmic complexity through targeted processing
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the generation of detailed three-dimensional models of structures, including interior and exterior features, with the capability to identify specific structural elements and damage, enhancing accuracy and utility in various industries.
Implementation Method 1
laser range data (point cloud data), LIDAR
Data Source
AI summary
Described in detail herein are systems and methods for generating computerized models of structures using geometry extraction and reconstruction techniques. The system includes a computing device coupled to a input device. The input device obtains raw data scanned by a sensor. The computing device is programmed to execute a data fusion process is applied to fuse the raw data, and a geometry extraction process is performed on the fused data to extract features such as walls, floors, ceilings, roof planes, etc. Large- and small-scale features of the structure are reconstructed using the extracted features. The large- and small-scale features are reconstructed by the system into a floor plan (contour) and/or a polyhedron corresponding to the structure. The system can also process exterior features of the structure to automatically identify condition and areas of roof damage.