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Pavement surface texture plays an essential role in skid resistance, tire–pavement interaction, and wet-weather driving safety. Conventional evaluations of pavement texture are primarily based on two-dimensional (2D) profile measurements, such as Mean Texture Depth (MTD) and Mean Profile Depth (MPD), which cannot fully represent the complex three-dimensional (3D) characteristics of pavement surfaces. As a result, the relationship between surface texture and wet pavement friction is often insufficiently described. Recent advances in high-resolution optical scanning enable detailed acquisition of surface topography and provide opportunities for more accurate texture quantification. This study proposes a comprehensive framework for characterizing micro- and macro-surface textures using high-resolution 3D scanning combined with both 2D and 3D analytical methods. Dense point-cloud data were collected from concrete pavement surfaces, and multiple longitudinal and transverse profiles were extracted. Fast Fourier Transform (FFT)-based filtering was applied to separate micro- and macro-texture components, and representative texture parameters were calculated from both profile-based and surface-based analyses. Wet pavement friction was evaluated using the British Pendulum Number (BPN), and statistical relationships between texture parameters and friction were examined. The results demonstrate that the proposed approach effectively captures multi-scale texture features and provides improved correlation with wet friction compared with traditional 2D methods. The developed methodology offers a practical basis for texture-based friction evaluation and pavement safety assessment.
