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Three-dimensional (3D) reconstruction of trees has always been a key task inprecision forestry management and research. Due to the complex branchmorphological structure of trees themselves and the occlusions from tree stems,branches and foliage, it is difficult to recreate a complete three-dimensionaltree model from a two-dimensional image by conventional photogrammetricmethods. In this study, based on tree images collected by various cameras indifferent ways, the Neural Radiance Fields (NeRF) method was used forindividual tree reconstruction and the exported point cloud models are comparedwith point cloud derived from photogrammetric reconstruction and laser scanningmethods. The results show that the NeRF method performs well in individual tree3D reconstruction, as it has higher successful reconstruction rate, betterreconstruction in the canopy area, it requires less amount of images as input.Compared with photogrammetric reconstruction method, NeRF has significantadvantages in reconstruction efficiency and is adaptable to complex scenes, butthe generated point cloud tends to be noisy and low resolution. The accuracy oftree structural parameters (tree height and diameter at breast height)extracted from the photogrammetric point cloud is still higher than those ofderived from the NeRF point cloud. The results of this study illustrate thegreat potential of NeRF method for individual tree reconstruction, and itprovides new ideas and research directions for 3D reconstruction andvisualization of complex forest scenes.

Evaluating the point cloud of individual trees generated from images based on Neural Radiance fields (NeRF) method