Annotated 3D Model Generation Using Computer Vision
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Solution Overview
Problem
Current systems for creating computer models of structures and properties lack the ability to rapidly generate annotated models that accurately represent real-world structures with rich information, relying on manual processes and inefficient data collection.
Innovation Solution
A system utilizing computer vision techniques to automatically identify candidate objects in 3D models, generate user interface screens for annotation, and dynamically adjust questions to quickly gather and associate relevant information, enabling rapid development of annotated models for repair and reconstruction purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual processes are used to create annotated computer models of structures, then detailed information can be obtained, but the time and effort required for data collection increases significantly
Solution Approach 1:
The system uses aerial imagery and computer vision to automatically generate 3D models and identify candidate objects, creating a digital copy of the physical structure that can be annotated without requiring physical inspection. This copying approach preserves annotation information while dramatically reducing data collection time.
Solution Approach 2:
The system replaces manual mechanical processes of physical inspection and data collection with automated computer vision algorithms and AI-based object identification. The computer vision system automatically processes aerial imagery to generate 3D models and identify objects, substituting human effort with automated computational processes.
2Productivity
If automated computer vision techniques are used to generate 3D models, then model creation is faster, but the ability to obtain rich annotation information is reduced
Solution Approach 1:
The system segments the annotation process into distinct phases: automatic 3D model generation from aerial imagery, automatic identification of candidate objects using computer vision, and targeted information gathering through dynamically generated user interface screens. This segmentation allows rapid model creation while preserving opportunities for detailed annotation.
Solution Approach 2:
The system dynamically adjusts the information gathering process based on automatically identified candidate objects. User interface screens are generated in real-time with questions tailored to the specific objects detected, allowing the system to adaptively collect rich annotation information while maintaining rapid processing speeds.
3Measurement precision
If comprehensive annotation information is gathered for all candidate objects, then model accuracy improves, but the complexity of the data collection process increases
Solution Approach 1:
The system performs preliminary automatic identification of candidate objects using computer vision before the annotation phase. By pre-identifying objects such as roofs, walls, windows, and doors from aerial imagery, the system prepares the data structure in advance, reducing the complexity of the subsequent annotation process while ensuring comprehensive coverage.
Solution Approach 2:
The system uses feedback from automatic object identification to dynamically generate targeted information gathering questions. The computer vision system's detection results feed into the user interface generation process, which creates context-specific questions only for identified candidate objects, reducing unnecessary data collection steps while maintaining model accuracy.
Data Source
AI summary
Systems and methods for rapidly developing annotated computer models of structures and properties is provided. The system generates three-dimensional (3D) models of structures and property using a wide variety of digital imagery, and/or can process existing 3D models created by other systems. The system processes the 3D models to automatically identify candidate objects within the 3D models that may be suitable for annotation, such as roof faces, chimneys, windows, gutters, etc., using computer vision techniques to automatically identify such objects. Once the candidate objects have been identified, the system automatically generates user interface screens which gather relevant information related to the candidate objects, so as to rapidly obtain, associate, and store annotation information related to the candidate objects. When all relevant annotation information has been gathered and associated with model objects, the system can create a list of materials that can be used for future purposes, such as repair and/or reconstruction of real-world structures and property.