Jhuang J.S.JEN-JER JAW2025-05-172025-05-172024-11-17https://www.scopus.com/record/display.uri?eid=2-s2.0-85217778357&origin=recordpagehttps://scholars.lib.ntu.edu.tw/handle/123456789/729391Point cloud, known for the ability to describe real-world scenes, have been widely used to obtain high-precision spatial information. However, surveying and mapping tasks from point cloud are often challenging due to noise, occlusion, sparsity, density changes, etc. which usually reduce efficiency and leave so many hard tests. At the same time, the 3D mesh model is a kind of data structure generated from the point cloud and consists of interconnected triangles or other polygonal elements. Compared with discrete point cloud, mesh models provide a more refined geometric and topological description, but the simplification and smoothing of data during generation may lead to the disappearance of target features. Although both point cloud and mesh models can be utilized in surveying and mapping tasks, each has its own advantages and disadvantages. To enhance the efficiency, accuracy, and completeness of spatial information extraction, this research introduces mesh models into the point cloud as references for visual quality, particularly relying on the geometric representation provided by meshes. It aims to analyze how the data between the point cloud and the mesh models correspond to and complement each other’s disadvantages for different targets and scenarios. Eventually, through these analyses, the potential development opportunities and concerns of point cloud and mesh models were identified, further enhancing the accuracy and efficiency of surveying and mapping work.falseMapping3-D mesh modelsCloud modelingCloud-basedDensity changeHigh-precisionMesh modelingPhotogrammetric point cloudPoint-cloudsReal-worldSpatial informationsMesh generationconference paper2-s2.0-85217778357