Evaluating the point cloud of individual trees generated from images
  based on Neural Radiance fields (NeRF) method
Hongyu Huang Guoji Tian Chongcheng Chen
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 reconstruction and the exported point cloud models are compared
with point cloud 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 higher successful reconstruction rate, better
reconstruction in the canopy area, it requires less amount of images as input.
Compared with photogrammetric reconstruction method, NeRF has significant
advantages in reconstruction efficiency and is adaptable to complex scenes, but
the generated point cloud tends to be noisy and 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 of
derived from the NeRF point cloud. The results of this study illustrate the
great potential of NeRF method for individual tree reconstruction, and it
provides new ideas and research directions for 3D reconstruction and
visualization of complex forest scenes.