3D Structure Modeling via 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 such as cameras and LIDAR, applying data fusion, geometry extraction, and reconstruction to create detailed 3D models, including large- and small-scale features, and identify damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing methods are used, then system complexity is reduced, but model accuracy and feature extraction capability deteriorate
Solution Approach 1:
The system segments the complex modeling process into distinct modules: data acquisition from multiple sensors, data fusion processing, geometry extraction, and 3D reconstruction. Each module handles specific tasks independently, improving overall accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The system transitions from 2D sensor data to 3D spatial models by integrating multi-dimensional data from cameras, LIDAR, and inertial sensors. This dimensional transformation enables accurate three-dimensional reconstruction and feature extraction that cannot be achieved with traditional 2D processing methods.
2Loss of information
If comprehensive sensor data is collected, then model completeness improves, but data processing time increases
Solution Approach 1:
The system performs preliminary data fusion and preprocessing of sensor data during the acquisition phase, organizing raw data from multiple sources into structured formats before the actual 3D reconstruction. This preliminary organization reduces the computational burden during subsequent processing stages, maintaining completeness while reducing overall processing time.
Solution Approach 2:
The system implements continuous data processing pipelines where sensor data is continuously acquired, fused, and processed in real-time or near-real-time. This continuous action ensures no information is lost while minimizing idle time between data collection and model generation, improving both completeness and efficiency.
3Measurement precision
If detailed geometry extraction is performed, then feature identification accuracy improves, but computational requirements increase
Solution Approach 1:
The system applies local quality processing by focusing detailed geometry extraction only on regions of interest or areas with significant features, rather than uniformly processing entire datasets. This selective approach maintains high feature identification accuracy for critical areas while reducing overall computational energy requirements.
Solution Approach 2:
The system performs partial geometry extraction by identifying and processing only the essential features needed for accurate 3D modeling, rather than extracting every possible geometric detail. This partial action approach achieves sufficient feature identification accuracy while significantly reducing computational energy consumption compared to exhaustive extraction methods.
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 rapid generation of precise 3D models of structures, allowing for the identification of features and damage, enhancing accuracy and efficiency in industries relying on structural data.
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.