3D Building Facade Mapping With Dynamic Texture Updates
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Solution Overview
Problem
Existing 3D maps have limited texture resolution and geometry quality, are difficult to update, and lack robust real-time image data analytics for consumer and commercial use cases.
Innovation Solution
A system for managing geospatial imagery that processes images to create accurate 3D building models, allowing for real-time updating and registration of building facades using computer vision techniques, including user interaction and machine learning for refining textures and removing obstructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D mapping methods using aerial imagery or specialized camera-equipped vehicles are used, then 3D maps can be generated, but the texture resolution and geometry quality are limited
Solution Approach 1:
The system enables dynamic updates of 3D building models by processing newly captured images through the same computer vision pipeline, allowing the map to evolve over time rather than requiring complete regeneration. This resolves the contradiction by making the system adaptable and updateable without sacrificing quality.
Solution Approach 2:
The system performs preliminary processing of images to extract building features, textures, and geometric data before final model construction. This preliminary extraction of high-quality data enables both high resolution and easier updates, as only new images need processing rather than complete remapping.
2Productivity
If traditional 3D mapping systems are used, then basic 3D representations can be created, but robust real-time image data analytics are not provided
Solution Approach 1:
The computer vision database platform serves multiple functions: it stores 3D building models, processes new images for updates, performs real-time analytics on image data, and provides consumer and commercial services. This multi-functionality resolves the contradiction by integrating analytics capabilities into the existing system architecture rather than adding separate complex systems.
Solution Approach 2:
The system automatically processes and analyzes image data through computer vision algorithms without requiring manual intervention for each analytics task. The database platform self-services by continuously ingesting images, updating models, and generating analytics, reducing the complexity burden on users while maintaining high productivity.
3Measurement precision
If detailed 3D building models with high texture resolution are created, then accurate building representations are achieved, but the system becomes difficult to update
Solution Approach 1:
The system segments the 3D building model into distinct components (geometry, textures, semantic features) that can be independently updated. When new images are processed, only the relevant segments need updating rather than the entire model, making high-accuracy models easy to maintain and update.
Solution Approach 2:
The system changes parameters such as texture resolution and geometric detail based on the quality and quantity of available image data. This allows the model accuracy to adapt dynamically, achieving high precision when sufficient data exists while maintaining ease of update when data is limited or changes are minor.
Data Source
AI summary
A method and related software are disclosed for processing imagery related to three dimensional models. To display new visual data for select portions of images, an image of a physical structure such as a building with a façade is retrieved with an associated three dimensional model for that physical structure according to common geolocation tags. A scaffolding of surfaces composing the three dimensional model is generated and regions of the retrieved image are registered to the surfaces of the scaffolding to create mapped surfaces for the image. New image data such as texture information is received and applied to select mapped surfaces to give the retrieved image the appearance of having the new texture data at the selected mapped surface.