Directed Image Capture for 3D Building Models
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Solution Overview
Problem
Existing methods for generating 3D models of buildings via aerial imagery or camera-equipped vehicles result in models with limited texture resolution, geometry quality, and are expensive, time-consuming, and difficult to update, while lacking robust real-time image data analytics for consumer and commercial use cases.
Innovation Solution
A directed image capture system that uses image processing servers, capture devices, and viewer devices to guide users in capturing high-quality ground-level images from multiple angles, employing graphical overlays and machine learning algorithms to assess image quality and provide corrective feedback, ultimately generating accurate 3D building models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If aerial imagery or camera-equipped vehicles are used to generate 3D models, then coverage area is improved, but texture resolution and geometry quality deteriorate
Solution Approach 1:
The system segments the image capture process into multiple ground-level viewpoints distributed across the building perimeter. Instead of relying on a single aerial pass, multiple capture devices or sequential captures from different ground positions contribute to the same 3D model, thereby segmenting the coverage area while maintaining high resolution through proximity to the building facade.
2Area of stationary object
If aerial imagery methods are used, then coverage area is improved, but time consumption and cost increase
Solution Approach 1:
The system employs automated graphical overlay guides that self-adjust based on detected building features and capture progress. The machine learning algorithms automatically assess image quality and provide corrective feedback without human intervention, enabling users to independently complete accurate captures. This automation reduces time consumption by eliminating manual positioning and quality assessment steps.
3Ease of operation
If traditional capture methods are used, then simplicity of operation is improved, but image quality assessment and guidance capability deteriorate
Solution Approach 1:
The system implements real-time feedback loops where machine learning algorithms continuously assess captured images against quality criteria and provide graphical overlay guidance for corrective actions. This feedback mechanism maintains simplicity for users while dramatically improving image quality assessment accuracy through automated computer vision analysis of focus, exposure, angle, and completeness metrics.
Data Source
AI summary
A process is provided for guiding a capture device (e.g., smartphone, tablet, drone, etc.) to capture a series of images of a building. Images are captured as the camera device moves around the building—taking a plurality of images (e.g., video) from multiple angles and distances. Quality of the image may be determined to prevent low quality images from being captured or to provide instructions on how to improve the quality of the image capture. The series of captured images are uploaded to an image processing system to generate a 3D building model that is returned to the user. The returned 3D building model may incorporate scaled measurements of building architectural elements and may include a dataset of measurements for one or more architectural elements such as siding (e.g., aluminum, vinyl, wood, brick and/or paint), windows, doors or roofing.