To improve the precision and effectiveness of crown-volume measurement and calculation, the authors have analyzed the characteristics of existing methods for processing the point cloud and have proposed a crown-surface reconstruction algorithm using a triangulated irregular network and voxel-based volumetric algorithms. This algorithm, after reconstructing the surface of the point-cloud crown, can extract the crown volume. This paper compares classic Delaunay grid-construction results with those from the proposed algorithm using a visualization method and carries out algorithm complexity analysis. These efforts have confirmed that the method presented in this paper is better than the traditional algorithm from the viewpoints of grid-construction accuracy and efficiency. This research, examined 30 trees in the study area. T-LiDAR was used to obtain point-cloud data for the crown. The classical manual dendrometric method, the point-cloud measurement method, the classical Delaunay algorithm, and the method proposed in this paper were used to calculate crown volume, and the results were compared. The four methods showed a good correlation (R2>=0.831), while the improved Delaunay method presented in this paper achieved good precision, good stability, and the least calculation time. The results of these experiments proved that the proposed algorithm has a considerable advantage in crown-volume extraction from point clouds (especially from T-LiDAR data). The combination of the proposed algorithm with T-LiDAR data could extract crown properties such as surface area and biomass quickly and precisely.