Three-dimensional (3D) reconstruction of trees has always been a key task in precision forestry management and research. Due to the complex branch morphological structure of trees themselves and the occlusions from tree stems, branches and foliage, it is difficult to recreate a complete three-dimensional tree model from a two-dimensional image by conventional photogrammetric methods. In this study, based on tree images collected by various cameras in different ways, the Neural Radiance Fields (NeRF) method was used for individual tree dense reconstruction and the exported point cloud models are compared with point clouds derived from photogrammetric reconstruction and laser scanning methods. The results show that the NeRF method performs well in individual tree 3D reconstruction, as it has a higher successful reconstruction rate, better reconstruction in the canopy area and requires less images as input. Compared with the photogrammetric dense reconstruction method, NeRF has significant advantages in reconstruction efficiency and is adaptable to complex scenes, but the generated point cloud tend to be noisy and of low resolution. The accuracy of tree structural parameters (tree height and diameter at breast height) extracted from the photogrammetric point cloud is still higher than those derived from the NeRF point cloud. The results of this study illustrate the great potential of the NeRF method for individual tree reconstruction, and it provides new ideas and research directions for 3D reconstruction and visualization of complex forest scenes.

The accurate and efficient 3D reconstruction of trees is beneficial for urban forest resource assessment and management. Close-range photogrammetry (CRP) is widely used in the 3D model reconstruction of forest scenes. However, in practical forestry applications, challenges such as low reconstruction efficiency and poor reconstruction quality persist. Recently, novel view synthesis (NVS) technology, such as neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS), has shown great potential in the 3D reconstruction of plants using some limited number of images. However, existing research typically focuses on small plants in orchards or individual trees. It remains uncertain whether this technology can be effectively applied in larger, more complex stands or forest scenes. In this study, we collected sequential images of urban forest plots with varying levels of complexity using imaging devices with different resolutions (cameras on smartphones and UAV). These plots included one with sparse, leafless trees and another with dense foliage and more occlusions. We then performed dense reconstruction of forest stands using NeRF and 3DGS methods. The resulting point cloud models were compared with those obtained through photogrammetric reconstruction and laser scanning methods. The results show that compared to photogrammetric method, NVS methods have a significant advantage in reconstruction efficiency. The photogrammetric method is suitable for relatively simple forest stands, as it is less adaptable to complex ones. This results in tree point cloud models with issues such as excessive canopy noise and wrongfully reconstructed trees with duplicated trunks and canopies. In contrast, NeRF is better adapted to more complex forest stands, yielding tree point clouds of the highest quality that offer more detailed trunk and canopy information. However, it can lead to reconstruction errors in the ground area when the input views are limited. The 3DGS method has a relatively poor capability to generate dense point clouds, resulting in models with low point density, particularly with sparse points in the trunk areas, which affects the accuracy of the diameter at breast height (DBH) estimation. Tree height and crown diameter information can be extracted from the point clouds reconstructed by all three methods, with NeRF achieving the highest accuracy in tree height. However, the accuracy of DBH extracted from photogrammetric point clouds is still higher than that from NeRF point clouds. Meanwhile, compared to ground-level smartphone images, tree parameters extracted from reconstruction results of higher-resolution and varied perspectives of drone images are more accurate. These findings confirm that NVS methods have significant application potential for 3D reconstruction of urban forests.

Accurate and efficient 3D reconstruction of trees is beneficial for urban forest resource assessment and management. Close-Range Photogrammetry (CRP) is widely used in 3D model reconstruction of forest scenes. However, in practical forestry applications, challenges such as low reconstruction efficiency and poor reconstruction quality persist. Recently, Novel View Synthesis (NVS) technology such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) has shown great potential in the 3D reconstruction of plants using some limited number of images. However, existing research typically focuses on small plants in orchards or individual trees. It remains uncertain whether this technology can be effectively applied in larger, more complex stands or forest scenes. In this study, we collected sequential images of urban forest plots with varying levels of complexity using different imaging devices. We then performed dense reconstruction of forest stand using NeRF and 3DGS methods. The resulting point cloud models were compared with those obtained through photogrammetric reconstruction and laser scanning methods. The results show that compared to photogrammetric method, NVS methods have a significant advantage in reconstruction efficiency. Photogrammetric method is less suited to more complex forest stands, resulting in tree point cloud models with issues such as excessive canopy noise, wrongfully reconstructed trees with duplicated trunks and canopies. In contrast, NeRF is better adapted to more complex forest stands, especially in reconstructing canopy regions. However, it can lead to reconstruction errors in the ground area when the input views are limited. The 3DGS method has a relatively poor capability to generate dense point clouds, resulting in models with low point density, particularly with sparse points in the trunk areas, which affects the accuracy of the diameter at breast height (DBH) estimation. Tree height and crown diameter information can be extracted from the point clouds reconstructed by all three methods, with NeRF achieving the highest accuracy in tree height. However, the accuracy of DBH extracted from photogrammetric point clouds is still higher than that from NeRF point clouds. Meanwhile, compared to ground-level smartphone images, tree parameters extracted from reconstruction results of higher-resolution and varied perspectives of drone images are more accurate. These findings suggest that NVS methods have significant potential for 3D reconstruction of urban forests, providing further technical support for forest resource visualization, inventory and management tasks.

Accurate and efficient 3D reconstruction of trees is crucial for forest resource assessments and management. Close-Range Photogrammetry (CRP) is commonly used for reconstructing forest scenes but faces challenges like low efficiency and poor quality. Recently, Novel View Synthesis (NVS) technologies, including Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have shown promise for 3D plant reconstruction with limited images. However, existing research mainly focuses on small plants in orchards or individual trees, leaving uncertainty regarding their application in larger, complex forest stands. In this study, we collected sequential images of forest plots with varying complexity and performed dense reconstruction using NeRF and 3DGS. The resulting point clouds were compared with those from photogrammetry and laser scanning. Results indicate that NVS methods significantly enhance reconstruction efficiency. Photogrammetry struggles with complex stands, leading to point clouds with excessive canopy noise and incorrectly reconstructed trees, such as duplicated trunks. NeRF, while better for canopy regions, may produce errors in ground areas with limited views. The 3DGS method generates sparser point clouds, particularly in trunk areas, affecting diameter at breast height (DBH) accuracy. All three methods can extract tree height information, with NeRF yielding the highest accuracy; however, photogrammetry remains superior for DBH accuracy. These findings suggest that NVS methods have significant potential for 3D reconstruction of forest stands, offering valuable support for complex forest resource inventory and visualization tasks.