## 1 Introduction
In the method of total station free-stationing, measuring the plane position and the rail's top elevation of the track centerline can achieve accurate results using the total station and level [18]. However, there are two problems with the free-stationing method of the total station. First, it is difficult to accurately find the steel centerline, which was measured with a steel ruler or a gauge. This method was time-consuming, laborious, and low in measurement accuracy. Second, the total station must be placed within the visible range during the entire measurement. It is essential to set a lot of transfer points where the measuring track does not have visibility or has no proper place for a measuring prism. Hence, the method of total station free-stationing is suitable for a single curve, but not for an entire continuous railway containing multiple curve units. [PERSON] successfully applied real-time kinematic (RTK), which is a global navigation satellite system (GNSS) carrier phase measurement technology, to an existing railway resurvey with high efficiency and high accuracy [19]. The above two problems existing in total station free-stationing still cannot be ignored. Whether using the involute-based method or coordinate-based measurement, working on the railway is inevitable. With the enhancement of the railway running speed and enlarging of traffic density, the \"skylight\" operation time is too short for a survey, which creates the need for a noncontact measuring method. Traditional methods including the string lining method, the deflection method, the free-stationing method, and RTK face great challenges that are unsuitable for the gradual development trend of high-speed railways. At present, due to speed, safety, and low cost, three-dimensional laser scanning technology has been applied effectively to solve these problems. In particular, point cloud data can provide detailed information in track detection and support railway reconstruction. Scholars have done a lot of research on using measurement scheme design, point cloud data processing,and feature extraction to develop a reconstruction model. [PERSON] and [PERSON] [20] proposed a method that reconstructed an entire railway environment successfully from point cloud datasets. However, the goal was to produce a final visualization of railway environments, and rail roads were considered part of the ground. [PERSON] and [PERSON] [21] presented an automated method to detect roads from mobile laser scanning (MLS) point cloud data. Both the geometry and intensity data of railway roads were utilized to extract track points and to model roads. [PERSON] et al. [22] compared the quality of the scans from a phase-based scanner and a hybrid time-of-flight scanner by fitting different sections of the track profile to its matching standardized rail model. However, both scanners were so sensitive to noise and artefacts that the proposed method was not robust. [PERSON] et al. [23] proposed a new approach that uses terrestrial laser scanning (TLS) to detect subsidence and irregularities in a track by fitting boundaries of the cross section of the track. The results indicated that the subsidence difference between TLS and precise leveling was 2 to 3 mm and the difference in the geometric parameters of the tracks was 1 to 2 mm. The approach, however, is not automated. [PERSON] et al. [24] fitted a parametric model of a rail piece to the points along each track and estimated the position and orientation parameters of each piece's model. This method is not suitable for highly detailed measurements with millimeter precision. Moreover, when reconstructing a complex railway environment, the complexity is based on the diversity of the objects of the railroad infrastructure and surroundings, which include railroads, buildings, power lines, pylons, street/traffic lights, and so forth. The fusion of light detection and ranging (LiDAR) data and images can achieve good results [25].