Abstract: To solve the problem that high density variation of long-range terrestrial laser scanning(TLS) point cloud, a density adaptive segmentation algorithm for extracting building plane was proposed in this paper. Firstly, the dynamic neighborhood search range was constructed based on the estimated theoretical point space, and the optimal neighborhood can be selected by internal indexes and Shannon entropy. Then, the dimensionality feature was calculated by using this neighborhood. Secondly, the region growing algorithm rules were set according to the optimal neighborhood, normal vector, dimensionality feature and point-to-plane distance to extract preliminary plane segmentation results. Finally, the segmentation result was optimized by patch merging, and then was tested on a single-site TLS data with scanning distance of 1 km. The result shows that the precision reaches 95%, the recall reaches 92%. This method can segment the building plane in the long-range TLS point cloud effectively. Compared with the traditional Shannon entropy method, the dynamic neighborhood search range used in this paper can significantly improve the efficiency of the algorithm. This method can efficiently and accurately extract the building planes from wide scene, and provide a reference for urban 3-D modeling.