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

Abstract

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.

Plant phenotyping relevance

NeRFによる個体樹木の3D再構成を中心に、写真測量・レーザースキャンと比較検証し、樹高や胸高直径という植物形質を抽出しているため。

Code and data availability

The paper's tree image datasets (Tree_1_phone, Tree_2_phone/nikon/uav) and TLS reference point clouds are the paper-specific phenotyping assets, but the authors state they are not publicly deposited: 'Data will be available upon reasonable request.' All URLs in the text (COLMAP, nerfstudio, CloudCompare, Lidar360, 3DFo

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