Vehicle Damage Mapping From 2D Images to 3D Models
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Solution Overview
Problem
Existing methods fail to accurately identify and visualize damage on vehicles from 2D images, which can affect potential buyers' interest in purchasing used vehicles.
Innovation Solution
A system that maps damage from a 2D image of a vehicle to a 3D computer model using image processing and machine learning to detect damaged areas, aligns and scales the images, and generates a GUI displaying the damage on the 3D model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If damage detection is performed using 2D images only, then the process is simple and quick, but the accuracy and visual understanding of damage location is insufficient
Solution Approach 1:
The patent transforms 2D image data into 3D spatial information by mapping detected damage locations from 2D images onto a 3D vehicle model. This dimensional transition enables precise damage visualization and location identification while maintaining the simplicity of 2D image capture, resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The 3D vehicle model serves as an intermediary between the 2D image and the final damage visualization. The system uses the 3D model to bridge the gap between simple 2D imaging and accurate damage representation, allowing precise damage location without requiring complex 3D imaging equipment.
2Productivity
If manual inspection of vehicle damage is performed, then accuracy can be maintained, but time consumption and labor costs increase
Solution Approach 1:
The system enables automatic self-inspection of vehicle damage by processing 2D images through machine learning models that automatically detect and map damage without human intervention. This automation maintains high detection accuracy while significantly increasing inspection speed and reducing labor requirements.
Solution Approach 2:
The patent replaces manual mechanical inspection with automated image processing and machine learning algorithms. The system uses computational methods to detect and map damage, substituting human inspectors with automated digital processing that achieves both speed and accuracy.
3Measurement precision
If 3D imaging is used to capture vehicle damage, then visualization accuracy is improved, but the complexity and cost of the system increases
Solution Approach 1:
The system creates a digital copy of the 3D vehicle model and maps 2D image damage data onto this virtual replica. This copying approach allows precise damage visualization without requiring actual 3D imaging hardware, maintaining accuracy while reducing system complexity and cost.
Data Source
AI summary
Aspects disclosed provide systems and methods for mapping damage from a two-dimensional (2D) image of a vehicle to a three-dimensional (3D) computer model of the vehicle. The system achieves this through various transformations and translation of pixel positions in the 2D image to a 3D computer model. Once the transformation and translations are performed, a graphical user interface (GUI) may be rendered indicating any damaged areas.