Aiming at the problem that the traditonal cloth simulation filtering (CSF) algorithm cannot distinguish the local microtopography of pavement damage, which leads to the wrong detection and omission of pothole damages, an adaptive descend distance CSF algorithm for pavement pothole extraction is proposed. First, the proposed algorithm preprocesses and denoises the point cloud of the road to obtain the pavement point cloud. Second, by improving the displacement distance of the"external force drop"and"internal force pull back"processes of the simulated cloth in the CSF algorithm, the adaptive distance drop of the simulated cloth is realized, and then further constructs the accurate local datum plane of the road surface and generates the depth-enhanced information model of the point cloud. Finally, depth threshold classification and Euclidean clustering algorithm are used to achieve precise detection of potholes and extract geometric attribute features of potholes. Experiments and analysis of the measured road data show that, the recall of potholes in the measured data reaches 83.3%, and the precision reaches 87.5%, the maximum relative error of area is 17.699%, and the maximum relative error of depth is 9.677%, which has a certain degree of robustness and applicability. The proposed algorithm can provide a powerful support for the work of large-scale three-dimensional pavement point cloud data for the automatic and precise detection of potholes on pavements.