This research conducts a quantitative analysis on the macrotexture of asphalt concrete pavement based on three-dimensional (3D) point cloud data. A binocular stereovision-based 3D point cloud data collection system is developed. The system is composed of a packaged component that includes a lighting source and two cameras, a dark shading cloth, and the computer control side with the configuration of the operation interface. Meanwhile, specimens of both asphalt concrete and open graded friction course (OGFC) are prepared as the test specimens. Next, 3D point cloud data of the specimens are collected using the proposed system. The macrotexture information is then extracted using the robust Gaussian method. The macrotextures of the pavement surface are characterized by 10 indicators; profile arithmetic average deviation, profile root mean square difference, mean texture depth, profile skewness value, profile steepness, profile unevenness distance, profile peak distance, profile root mean square slope, profile root mean square wavelength, and surface roughness area ratio. At the same time, the friction coefficients of these specimens are measured by British Pendulum Number. Finally, the correlations between each indicator and the friction conditions of different specimens are assessed. Results demonstrate that the proposed macrotexture indicators are reliable for evaluating the friction conditions because significant correlations have been observed. Meanwhile, the correlations for the OGFC gradations are always higher than the asphalt concrete gradations. All the findings prove that the proposed quantitative indicators are effective for the characterization of the macrotexture of asphalt concrete pavement.