Abstract: A user device includes a camera configured to capture a series of images of a vehicle and a processor configured to receive a first image of the vehicle from a first viewpoint, classify, for the first image, one or more parts of the vehicle captured in the first image, generate, 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 with the first graphic indicating the parts of the vehicle being displayed in the first image.

Abstract: The present invention relates to verification of damage to vehicles. More particularly, the present invention relates to a universal approach to automated generation of a damage estimate to a vehicle using images of the vehicle and verification of a manually-generated damage repair proposals using the automatically generated damage estimate. Aspects and/or embodiments seek to provide a computer-implemented method of generating one or more repair estimates from one or more photos of a damaged vehicle and comparing the generated estimate(s) to one or more input repair estimates to verify the one or more input repair estimates.

Abstract: The present invention relates to assessing the damage and repairs needed to damaged vehicles. More particularly, the present invention relates to assessing vehicle damage using primarily photos of damaged vehicles and information provided by drivers or insurers, to determine whether vehicle body parts to be replaced or repaired require paint blending. Aspects and/or embodiments seek to provide a method and system to determine whether a part of a damaged vehicle requires paint blending.

Abstract: The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether each part of a vehicle should be classified as damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle including segmenting the input images. Aspects and/or embodiments seek to provide a computer-implemented method for determining damage states of each part of a damaged vehicle, indicating whether each part of the vehicle is damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle, using images of the damage to the vehicle and trained models to assess the damage indicated in the images of the damaged vehicle, including performing segmentation of the images to create richer input data.

Abstract: The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether determined damage to a vehicle is consistent with information provided as to the cause of the damage to the vehicle. Aspects and/or embodiments seek to provide a computer-implemented method for determining whether damage to a vehicle, which is determined using images of the damage to the vehicle, is consistent with information documenting the cause of the damage to the vehicle, for example insurance claim data or repair shop proposed repair data.

Abstract: The present invention relates to the determination of repair operations for a damaged vehicle. More particularly, the present invention relates to determining repair operations, for example whether to repair or replace parts of a damaged vehicle and associated labour time required, for a damaged vehicle using images of the damage to the vehicle. Aspects and/or embodiments seek to provide a computer-implemented method for determining repair operations that are required to repair a damaged vehicle, using images of the damage to the damaged vehicle.

Abstract: A method of determining, one or more damage states of one or more auxiliary parts of a damaged vehicle, the vehicle comprising a plurality of normalized parts and at least some of the normalized parts further comprising one or more auxiliary parts. The method includes receiving one or more images of the vehicle, using a plurality of classifiers, each determining at least one classification of damage to the vehicle, each said classification being determined for each of a plurality of normalized parts of the vehicle, determining one or more classifications for the plurality of auxiliary parts using one or more trained models, wherein each classification comprises at least one indication of damage to at least one auxiliary part and outputting the determined damage states of the one or more auxiliary parts.