With the continuous improvement of living standards in modern society, the demand for refined 3D object representation models has grown considerably. Concurrently, the diversification of methods for acquiring 3D point clouds has significantly enhanced our ability to digitally express the surrounding environment. Consequently, the efficient and straightforward generation of high-precision 3D mesh models from point clouds has become particularly vital. As a typical production method for 3D mesh models, point cloud surface reconstruction technology has gained widespread application across various industries owing to its convenience and efficiency. Therefore, in this paper, we propose a distance-constrained scale-space surface reconstruction algorithm, which utilizes the neighborhood distances of point clouds to constrain the anomalous triangular mesh generated by the scale-space reconstruction algorithm, thereby enabling the more precise and efficient surface reconstruction of point clouds. Initially, the raw point set obtained from 3D laser scanning undergoes preprocessing, followed by point cloud reconstruction and mesh construction using the distance-constrained scale-space algorithm. Finally, the constructed mesh is back-projected onto the original point set, resulting in a more refined 3D model. Experiments conducted on the urban underground tunnel dataset successfully reconstructed the surface model of the tunnels, validating the effectiveness of the proposed method and enhancing the visual quality of the model.