3D Model Change Detection Using Texture and Geometry Uncertainty
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Solution Overview
Problem
Current methods for detecting changes in environments, such as geographical information systems, are time-consuming and require manual comparison of maps or images, making it difficult to efficiently identify differences due to natural or human-induced changes.
Innovation Solution
A method that automatically matches and compares 3D models of environments based on geometrical and texture information, accounting for uncertainties to differentiate between changes caused by measurement errors and actual environmental changes, allowing for the identification of both additions and removals in the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual comparison of maps or images is used to detect changes in the environment, then measurement precision can be maintained, but productivity is significantly reduced and time consumption increases
Solution Approach 1:
The patent replaces manual mechanical comparison methods with an automated computer-based system that processes 3D models and images. The system uses algorithmic matching of geometric and texture information to automatically detect changes, substituting human operators with computational methods that maintain precision while dramatically improving productivity.
Solution Approach 2:
The patent transforms 2D images into 3D models with multiple parameters (geometric coordinates, texture information, uncertainty values) to enable more comprehensive change detection. By representing the environment in three dimensions with rich attribute data, the system achieves both high precision in detecting subtle changes and high productivity through automated processing of structured data.
2Productivity
If automated comparison methods are implemented to improve productivity, then time consumption is reduced, but measurement precision may deteriorate due to difficulty in detecting and measuring automatic changes
Solution Approach 1:
The patent introduces 3D models as an intermediary representation between raw images and change detection results. The 3D models serve as a standardized intermediate format that captures geometric and texture information in a structured manner, making automated comparison more reliable and accurate while maintaining high processing efficiency.
Solution Approach 2:
The patent performs preliminary processing to create 3D models with pre-computed geometric information, texture information, and uncertainty values before the actual change detection occurs. This preliminary structuring of data enables the automated comparison system to work more effectively, reducing the difficulty of detecting changes while maintaining high productivity.
3Measurement precision
If 3D models with geometrical and texture information are used to represent the environment, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex task of environment representation into distinct components: geometric information (shape, position, dimensions) and texture information (surface appearance, color). This segmentation allows the system to process and store each type of information separately, managing complexity while achieving high measurement precision through comprehensive data capture.
Data Source
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AI summary
The present disclosure relates to a method (700) and arrangement for identifying a difference between a 3D model of an environment and the environment as reproduced at another timing. The reproduction of the environment comprises at least one 2D image. Each point or part of the 3D model comprises geometrical and texture information. The method comprises the steps of: matching (706) corresponding points or parts of the 3D model and the at least one 2D image based on the texture information in the 3D model and texture information in the at least one 2D image, and determining (707) at least one difference value for the texture information for each corresponding part or point of the first 3D model and the at least one 2D image, wherein a geometrical information measurement uncertainty is associated to each point or part of the 3D model, and/or a texture information measurement uncertainty is associated to each point or part of the 3D model, said method further comprising a step of identifying a difference between the 3D model and the at least one 2D image based on the determined at least one difference value, and based on the geometrical information measurement uncertainty and/or the texture information measurement uncertainty.