Remote vehicle damage assessment

1. A user device, comprising:

a camera configured to capture a series of images of a vehicle;

a processor configured to perform operations comprising:

receiving a first image of the vehicle from a first viewpoint;

classifying, using an artificial intelligence (AI) model, one or more parts of the vehicle captured in the first image; and

generating, for the first image, a first graphic indicating the parts of the vehicle being displayed in the first image; and

a display configured to receive the first image and the first graphic from the processor and display the first image of the vehicle overlaid with the first graphic indicating the parts of the vehicle being displayed in the first image, wherein the first graphic comprises a two-dimensional representation of the vehicle showing at least one part of the vehicle that is not visible on a three-dimensional representation of the vehicle from a same point of view.

2. The user device of claim 1 , wherein the operations further comprise:

executing a web based application, wherein the classifying is based on information provided by the web based application.

3. The user device of claim 2 , wherein the information is a classifying artificial intelligence (AI) model that is executed on the user device.

4. The user device of claim 1 , wherein the first graphic comprises a confidence level associated with each part that is identified in the first image.

5. The user device of claim 1 , wherein the first graphic indicating the parts of the vehicle being displayed in the first image comprises text identifying each of the parts.

6. The user device of claim 1 , wherein each of the parts or a portion of the vehicle being displayed in the image are highlighted in the two-dimensional representation of the vehicle.

7. The user device of claim 1 , wherein the operations further comprise:

receiving a second image of the vehicle from a second viewpoint;

classifying, for the second image, one or more parts of the vehicle captured in the first image;

generating, for the second image, a second graphic indicating the parts of the vehicle being displayed in the second image,

wherein the display is further configured to receive the second image and the second graphic from the processor and display the second image of the vehicle with the second graphic indicating the parts of the vehicle being displayed in the second image.

8. The user device of claim 7 , wherein the display dynamically adjusts the display of the first image including the first graphic and the second image including the second graphic.

9. The user device of claim 1 , wherein the series of images comprise video images, and wherein the display displays an image with a corresponding graphic for each of the series of video images.

10. The user device of claim 1 , wherein the operations further comprise:

determining an angle of the camera relative to the vehicle, wherein the angle is displayed in the first graphic.

11. The user device of claim 1 , wherein the operations further comprise:

determining the series of images that are required to perform a complete evaluation of the vehicle; and

outputting instructions to be displayed on the display to instruct a user of the user device to collect the series of images.

12. A user device, comprising:

a camera configured to capture a series of images of a vehicle; and

a processor configured to perform operations comprising:

receiving a series of images of the vehicle from one or more viewpoints;

classifying, for each of the images, one or more parts of the vehicle captured in the image using a web-based application comprising a first artificial intelligence (AI) model for identifying the parts of the vehicle, wherein the first AI model generates confidence levels for identifying parts of the vehicle;

classifying, for each of the images, an extent of damage of the classified one or more parts of the vehicle captured in the images using the web-based application comprising a second AI model for identifying the damage of the vehicle; and

generating an estimate to repair the damage of the one or more parts of the vehicle.

13. The user device of claim 12 , wherein the first AI model is a multi-task model for identifying multiple parts of the vehicle in the image.

14. The user device of claim 12 , wherein the first and second AI models comprise a non-linear hierarchical algorithm, a neural network, a convolutional neural network, a recurrent neural network, a long short-term memory network, a multi-dimensional convolutional network, a memory network, a fully convolutional network, or a gated recurrent network.

15. The user device of claim 12 , wherein the first and second AI models generate respective confidence levels for the respective classifications.

16. The user device of claim 12 , wherein the first and second AI models comprise the same AI model.

17. The user device of claim 12 , wherein the web-based application receives information from the user device to guide the capture of images, the information comprising height information, distance information or vehicle information.

18. The user device of claim 12 , wherein the first and second AI models are agnostic with respect to identifying features of the vehicle including a make and model of the vehicle.

19. The user device of claim 12 , wherein the processor is configured to perform further operations comprising:

submitting a portion of the series of images to a remote server for further classification; and

receiving information from the remote server to inform the estimate to repair the damage of the vehicle.

20. The user device of claim 12 , wherein the processor is configured to perform further operations comprising:

generating a confidence level for the damage estimate; and

prompting a user of the user device to capture additional images when the confidence level for the damage estimate is below a predetermined threshold.