Recent advances in geomatics and 3D surveying have enabled the acquisition of increasingly dense point clouds through both static and mobile laser scanning systems, supporting the rapid digital documentation of built and natural environments. At the same time, the growing diffusion of WebGL technologies has opened new possibilities for the online visualization and dissemination of complex three-dimensional datasets. Within this context, the present study proposes an open-source workflow for the automatic extraction of significant geometric surfaces from laser scanner point clouds and their integration into a web-based visualization framework. The method was developed within a Python-based processing environment and tested on three datasets characterized by different levels of geometric complexity: a regular built environment, an under-construction building environment, and a historical context. The workflow includes point cloud preprocessing, automated segmentation strategies adapted to the geometric complexity of each case, extraction of planar and non-planar elements, polygonal surface generation, mesh construction, and conversion of outputs into lightweight formats suitable for web publication. The final visualization environment combines segmented polygonal models and subsampled point cloud data through open-source WebGL technologies. The results demonstrate that the proposed strategy provides a scalable and flexible solution for the rapid online representation of large laser scanner datasets, supporting surface recognition, low-cost accessibility, and future semantic enrichment within web-based geospatial and Digital Twin applications.