Tread geometry is one of the parameters that influence wear. Hence, investigating tread geometries and their evolution during tire usage becomes crucial for mitigating wear-related emissions. Among the state-of-the-art measurement methods, 3D scanning is notable for its ability to provide detailed insights into tread geometry. This study introduces a methodology for analysing the evolution of tire tread geometry, aiming to improve tire wear analysis. The proposed approach based on first derivative analysis and discrete data binning, was tested exploiting a benchmark tire generated virtually in a CAD software and sampling a point cloud on its surface, resembling 3D scanning acquisition. The results show its capability of leveraging 3D scanning technology to provide detailed insights into tread geometry by isolating sampled points located within grooves from those belonging to the outermost surface. The recognition of geometric features allows for the separate analysis of the different layers, extending the capability of average profile-based wear assessments to complex tread patterns, such as directional tires. Furthermore, it enables local analysis of tread geometry changes, facilitating the study of geometry evolution throughout the tire’s lifespan.