3D Structural Model Annotation With Automated Object Detection
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Solution Overview
Problem
Existing technologies struggle to rapidly create annotated computer models of structures that accurately represent real-world attributes and require manual annotation processes, which are time-consuming and inefficient.
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 gather relevant information, enabling rapid development of annotated models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation processes are used to create annotated computer models of structures, then accurate real-world attributes can be captured, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary automated annotation using computer vision techniques to pre-identify and tag objects, attributes, and relationships in the 3D model before final review. This preliminary action captures most annotations automatically, reducing the time required for manual completion while maintaining accuracy through subsequent verification steps.
Solution Approach 2:
The patent replaces manual mechanical annotation processes with automated computer vision and machine learning systems. The computer vision algorithms automatically detect, classify, and annotate structural elements, materials, and attributes, substituting human manual work with automated optical and computational systems that are both faster and equally accurate.
2Loss of information
If comprehensive annotation information is gathered for all candidate objects, then detailed and accurate models are produced, but the complexity of the annotation process increases
Solution Approach 1:
The annotation process is segmented into distinct automated stages: candidate object identification, attribute extraction, relationship detection, and validation. Each stage handles specific tasks independently, reducing overall process complexity while ensuring comprehensive information gathering. The system segments the model into hierarchical levels (structures, components, materials) and annotates each level separately.
Solution Approach 2:
The system introduces an intermediary automated processing layer between the 3D model and the final annotated output. This intermediary layer uses computer vision algorithms and machine learning models to bridge the gap between raw model data and comprehensive annotations, managing the complexity by handling information extraction, validation, and organization automatically before presenting results.
3Productivity
If automated computer vision techniques are used to identify candidate objects, then the speed of model creation increases, but manual review and verification may be reduced
Solution Approach 1:
The system implements feedback loops where automated annotations are continuously validated and refined. Computer vision algorithms generate initial annotations, which then feed into verification stages where discrepancies are detected and corrected. The feedback mechanism allows the system to learn from verification results and improve automated annotation accuracy over time, maintaining reliability while preserving high productivity.
Solution Approach 2:
The system performs preliminary automated annotation at high speed to rapidly create initial models, then applies verification procedures to critical elements. This preliminary action approach allows the majority of the model to be created quickly while focused verification ensures accuracy where needed, balancing productivity and reliability according to project requirements.
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. The system also allows for modeling of water damage of a structure, as well as generating lists of tasks for mitigating the water damage and associated costs.