With digital information applied in intelligent transportation system, pavement crack detection with digital twins has drawn widely attention since the past several years. However, it is still a challenge task to accomplish crack detection satisfactory results due to complex pavement background. This paper presents a pavement crack detection method based on 3D edge representation and data communication with digital twins. Firstly, pavement data is achieved by multiple sensors including video data and 3D scanning technology, and the digital twins based model is constructed based on data communication. Secondly, to distinguish between crack feature and pavement background in depth level, the point cloud data enhancement method based on fractional differential is proposed to strengthen crack boundary and maintain detail simultaneously. Thirdly, a 3D crack segmentation algorithm is proposed based on edge feature representation. After improved filter function with plane proximity and depth similarity, the gradient magnitude values of every point are estimated by the neighborhood and direction. Two thresholds are computed automatically using the iteration method to extract the crack from pavement. Finally, the dataset is established by 2600 crack data, and extensive experiments verify the proposed method could conduct better result compared with other existing methods using F-measure evaluation system.