High-definition (HD) map is one of the key assets for autonomous driving, which supports various modules such as behavior prediction and motion planning of autonomous vehicles by providing accurate and rich geometric and semantic information. However, at present, the scalability and computational efficiency of HD map generation cannot meet the needs of highly automated driving. Specifically, efficiently obtaining the optimal parameters of the road’s reference path is still an open problem. In this paper, we propose a fast and robust path reconstruction method, which compresses the dense points of a reference line into sparse parameters with minimal loss of information. The reconstructed path consists of segmented linear curvature contours, which are straight lines, circular arcs, and clothoids. The optimum result is obtained through linear programming for short-path reconstruction, and for the long paths, a fast progressive reconstruction approach is used to find a feasible solution. Experimental results on both randomly generated data and the GPS-collected trajectories show that compared with existing methods, the proposed method can generate more accurate path reconstruction, and the computational time is greatly reduced.