Abstract: A vehicle inspection system consolidates redundant multi-view defect detections using three-dimensional spatial clustering on CAD models. The system projects two-dimensional defect detections from multiple camera viewpoints onto three-dimensional CAD model surfaces through ray-casting algorithms, then clusters projected locations based on geodesic distances to identify unique physical defects. This approach eliminates detection redundancy while providing precise spatial localization, enabling consistent defect tracking across multiple inspections regardless of vehicle positioning variations.

Abstract: A computer-implemented method and a system for temporal damage consistency validation, comprising receiving, at one or more processors, a current vehicle scan containing image data and damage detections for a vehicle, retrieving historical damage data for the vehicle from previous scans;, projecting geometric positions of the previously detected damage regions onto corresponding locations in the current vehicle scan using a geometric transformation that accounts for vehicle positioning differences between inspection sessions, matching the projected damage positions with current scan detections using feature vector similarity scoring by computing similarity measures between deep learning feature vectors of projected damage regions and deep learning feature vectors of damage detections found in corresponding areas of the current vehicle scan and classifying damage states based on results of the matching operation.