Deformation monitoring of structures is a common application and one of the major tasks in surveying engineering, but there is a great challenge for the coordinate datum unification of multitemporal point cloud data. To address this problem, an automatic detection method of the virtual reference datum in multitemporal point cloud is proposed in this article. To obtain the corresponding grids of multitemporal point cloud, an appropriate coarse registration algorithm is adopted to approximately

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... ansform the multitemporal data into a unified coordinate system. Then, the stable areas are extracted based on the probability density functions similarity of the corresponding grids, which are defined as the virtual reference datum. Furthermore, an improved 3D normal distribution transform algorithm considering the cell boundaries and iteratively selecting an appropriate cell size is constructed to achieve fine registration of the virtual reference datum. Finally, the coordinate unification of the multitemporal point cloud is implemented according to the transformation parameters of the virtual reference datum. The proposed method is tested on a landslide point cloud, which is captured by static terrestrial laser scanning. The virtual reference datum extraction accuracy of the two-temporal landslide point cloud captured on the same day is 96%, and the coordinate unification accuracy is 3 mm. The experimental results demonstrate that the proposed method is effective in coordinate datum unification of multitemporal point cloud. Index Terms-Coordinate unification, point cloud, virtual reference datum. He is currently an Academician with the Russian Academy of Natural Sciences. His research interests include measurement data processing theory and method, remote sensing technology and application.