3D Vehicle Damage Reconstruction for Faster Claim Assessment
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Solution Overview
Problem
Conventional methods for assessing vehicle damage in insurance claims are time-consuming and result in inconsistent results due to the need for manual inspection, leading to delayed and potentially erroneous processing of claims.
Innovation Solution
A computing system utilizing machine learning models, such as Neural Radiance Fields (NeRF), to generate 3D scene reconstructions of vehicles based on image data, allowing remote assessment of damage through mobile devices and providing accurate damage analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used for vehicle damage assessment, then detailed examination can be performed, but the process becomes time-consuming and results in delayed claim processing
Solution Approach 1:
The system creates a digital 3D copy of the vehicle and its damage using photogrammetry technology. Multiple images captured by a mobile device are processed to generate an accurate digital replica, allowing remote experts to inspect the damage without physically visiting the scene, thus reducing time loss while maintaining assessment accuracy
Solution Approach 2:
The patent replaces the mechanical manual inspection process with an automated photogrammetry and image processing system. The system automatically captures images, processes them through algorithms to create 3D models, and enables remote digital inspection, substituting the manual mechanical process with an automated digital system that is both faster and equally accurate
2Measurement precision
If manual inspection is used for damage assessment, then detailed evaluation is possible, but consistency in results deteriorates due to human variability
Solution Approach 1:
The system enables the damage assessment process to serve itself through automation. The photogrammetry system automatically captures images, processes them through consistent algorithms, generates 3D models, and provides measurements without human intervention in the measurement process, ensuring that the same damage will always yield the same assessment results regardless of who operates the system
Solution Approach 2:
The patent replaces the variable human manual inspection process with a standardized automated digital system. By substituting human judgment with consistent image processing algorithms and 3D modeling techniques, the system eliminates human variability and ensures reliable, repeatable assessment results across different users and situations
3Productivity
If remote assessment through 3D reconstruction is implemented, then processing speed increases, but the complexity of the system increases
Solution Approach 1:
The system uses a mobile device that already exists in the user's possession, serving multiple functions: capturing images, storing data, and displaying results. This universal device approach avoids the need for specialized expensive equipment, reducing system complexity while maintaining high processing speed through the use of readily available technology
Solution Approach 2:
The system creates simplified digital copies (2D images and 3D models) of the physical vehicle damage. These digital replicas can be easily transmitted and processed, enabling fast remote assessment without requiring complex physical measurement equipment or on-site inspection infrastructure
Data Source
AI summary
In one aspect, an example method includes: (a) receiving a plurality of images of a particular vehicle; (b) generating an accident reconstruction model using one or more machine learning models, wherein the one or more machine learning models are configured to generate the accident reconstruction model using the received plurality of images, and wherein the accident reconstruction model indicates, for each of multiple regions on the particular vehicle, a respective extent of damage to the particular vehicle; (c) receiving a request for an accident reconstruction report for the particular vehicle; (d) based on the received request, identifying potential damage to the particular vehicle, wherein the identified potential damage is based on at least the generated accident reconstruction model; and (e) transmitting instructions that cause the mobile computing device to display a graphical indication of the potential damage to the particular vehicle.