Laser scanning systems are modern measurement techniques generating large datasets. Observations, usually collected as a point cloud, present the general results that can be visualized using specialized software. While the final effect might be impressive from a visualization point of view, it is inconvenient for modeling or extracting detailed information about, for example, terrain, buildings, engineering structures, and deformations. Therefore, data from laser scanning systems require post-processing using several methods reflecting different purposes or data processing stages: data segmentation, modeling, and filtration. Msplit estimation is one of the methods that has proved its effectiveness in laser scanning data processing and determination of terrain profiles, deformation, or building shapes. Processing the complete datasets tends to only yield often inadequate results when high-class computers are used, and it is time-consuming. Therefore, datasets tend to remain segmented. This paper explores a range of several types of segmentation methods that can be used in Msplit estimation. It presents profile determination when data cut out from the original point cloud are divided into intervals of the same length, or the sliding window algorithm is applied. In comparison, the given examples show that the latter approach can provide more reliable results. The application of the sliding window algorithm entails having to make assumptions concerning estimation parameters. The paper offers valuable guidance about both the width of the window and the slide size.