Abstract: The large number of bolts and screws attached to the subway shield ring plates, along with the great amount of accessories of metal stents and electrical equipments mounted on the tunnel walls, means that tunnel laser point cloud data includes lots of non-tunnel section points, referred to as non-points, therefore affecting the accuracy for modeling and deformation monitoring. This paper proposes a filtering method for point clouds based on the elliptic cylindrical model. The original laser point cloud data is projected onto a horizontal plane, and a searching algorithm is used to extract the edging points of both sides, to further to fit the tunnel central axis. Along the axis the point cloud is segmented regionally, and then fitted as smooth elliptic cylindrical surface by iteration. This processing enables automatic filtering of those inner wall non-points. Experiments on two groups of data showed coincident results, that the elliptic cylindrical model based method effectively filters out the non-points, thus providing high-quality point cloud for subway deformation monitoring.