3D Building Material Auto-Population from Ground Image Measurements
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Solution Overview
Problem
Existing 3D textured building models generated from aerial imagery suffer from limited texture resolution, geometry quality, inaccurate geo-referencing, high cost, and lack of real-time data analytics, making them inefficient for consumer and commercial use cases.
Innovation Solution
A system and method for auto-populating a materials ordering system by analyzing ground-level images using image processing servers to identify architectural elements, calculate dimensions, and estimate material requirements, utilizing techniques such as image recognition, deep learning, and geometric scaling to create accurate 3D building models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If aerial imagery is used to generate 3D building models, then coverage area is improved, but texture resolution and geometry quality deteriorate
Solution Approach 1:
The system segments the building model generation process into two distinct phases: aerial imagery is used for initial model creation and large-area coverage, while ground-level images are used for detailed texture and geometry refinement. This segmentation allows each method to operate in its optimal domain, achieving both broad coverage and high precision.
Solution Approach 2:
The patent merges aerial imagery and ground-level images into a unified 3D building model. The aerial data provides overall structure and coverage, while ground-level data enhances texture resolution and geometric accuracy. The combination of these two data sources resolves the contradiction between coverage area and manufacturing precision.
2Manufacturing precision
If specialized camera-equipped vehicles are used for 3D mapping, then texture resolution is improved, but cost and time consumption increase
Solution Approach 1:
The system applies partial action by using ground-level images only for specific building facades that require enhanced texture resolution, rather than capturing all buildings with expensive specialized vehicles. This selective approach achieves high texture quality where needed while reducing overall time and cost.
Solution Approach 2:
The system leverages freely available ground-level images from web sources to enhance building models, eliminating the need for expensive specialized camera-equipped vehicles. This self-service approach uses publicly accessible data to achieve high texture resolution without additional cost or time investment.
3Extent of automation
If traditional 3D mapping methods are used, then model generation is achieved, but real-time data analytics capability is lost
Solution Approach 1:
The system maintains continuous useful action by processing ground-level images in real-time alongside the 3D model generation process. Instead of completing model generation first and then analyzing data, the system continuously extracts analytics from ground-level images during the modeling process, preserving real-time data capabilities throughout the workflow.
Solution Approach 2:
The patent introduces ground-level images as an intermediary element that serves dual purposes: enhancing 3D model quality and enabling real-time data analytics. This intermediary data source bridges the gap between model generation and analytics, allowing both functions to operate simultaneously without compromising either.
Data Source
AI summary
Disclosed are systems and method for determining information related to building materials based on determined measurements from a multi-dimensional building model comprising features and elements embodying such materials and measurements. The multi-dimensional model may be based on a plurality of received images, such as ground-based images of a building. The multi-dimensional model may be scaled, or a scale extracted based on data of the model. The multi-dimensional model may comprise architectural elements, and the scale used to determine measurements of such architectural elements. With the scaled measurements of the architectural elements in the model, product information related to multi-dimensional model and its elements may be derived and combined in alternative means.