3D Roof Model Fine Adjustment for Aerial Image Alignment

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Solution Overview

Problem

Existing systems fail to accurately and efficiently refine the position and orientation of 3D roof structure models projected onto aerial images due to missing camera parameter information and inability to provide high-resolution estimates of overlap positions, leading to inaccurate 3D roof structure models.

Innovation Solution

A system that utilizes a metaheuristic method, specifically Variable Neighborhood Search (VNS), to optimize 3D roof structure models by scoring and refining parameters based on image alignment, using a weight function to align 3D roof structure candidates with aerial imagery.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If traditional 3D modeling systems are used to generate roof structure models from aerial images, then the modeling process can be automated, but the accuracy and alignment of the 3D model with the actual roof structure deteriorates due to missing camera parameters and inability to provide high-resolution overlap position estimates

Engineering Contradiction:

Improveautomation of 3D roof modelingVSAvoidalignment accuracy of 3D roof model

Core Design Contradiction:

Extent of automationVSManufacturing precision

Solution Approach 1:

The system performs preliminary actions by generating multiple candidate 3D roof structure models with different parameters before selecting the optimal one. This preliminary generation of alternatives allows the system to overcome the limitations of traditional single-model approaches and achieve better alignment accuracy through subsequent optimization.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies parameter changes by adjusting various parameters of the 3D roof structure model candidates, including position, orientation, and geometric properties. By systematically varying these parameters and evaluating alignment with aerial images, the system optimizes the model to achieve high precision despite missing camera parameter information.

Inventive Principle:

Principle #35Parameter changes

2Manufacturing precision

If multiple 3D roof structure model candidates are generated and evaluated, then the alignment accuracy can be improved, but the computational complexity and processing time increases

Engineering Contradiction:

Improvealignment accuracy of 3D roof modelVSAvoidcomputational complexity of model evaluation

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The system applies local quality by focusing computational resources on evaluating specific critical parameters and regions of the 3D model that have the greatest impact on alignment accuracy. Rather than uniformly processing all model aspects, the system identifies and optimizes key local features such as roof edges, corners, and overlapping regions, thereby reducing overall computational complexity while maintaining high precision.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20250285376A1Systems and Methods for Fine Adjustment of Roof Models

Publication Date: 2025.09.11 INSURANCE SERVICES OFFICE INC

AI summary

Systems and methods for fine adjustment of computerized roof models are provided. The system generates a 3D roof structure model based on at least one image obtained from an aerial imagery database. Alternatively, the system could retrieve at least one stored 3D roof structure model from a 3D roof structure model database. The system weighs (e.g., scores) each 3D roof structure model candidate and determines an optimal 3D roof structure model by applying a variable neighborhood search to a 3D roof structure model candidate having a highest confidence score among the weighed 3D roof structure model candidates.