华南农业大学学报2025，Vol.46Issue(4)：528-537,10.DOI:10.7671/j.issn.1001-411X.202410015
基于NeRF和改进RandLA-Net的果树三维重建与果实语义分割方法
3D reconstruction of fruit trees and fruit semantic segmentation based on NeRF and improved RandLA-Net
摘要
Abstract
[Objective]To solve the problem of accurate fruit segmentation in complex orchard environment.[Method]A novel method for 3D reconstruction citrus fruit trees and fruit semantic segmentation of was proposed.First,the implicit 3D representation of the fruit tree was learned from multi-view images using the neural radiance field(NeRF)technology,generating high-quality point cloud models of the fruit tree.Then,the improved random local point cloud feature aggregation network(RandLA-Net)was adopted to conduct end-to-end semantic segmentation of the fruit tree point cloud,accurately extracting the fruit point cloud.In this study,targeted improvements were made to RandLA-Net.A bilateral enhancement module was added after the encoder layer,and a loss function more suitable for the fruit point cloud segmentation task was adopted.The improved segmentation network was verified using the citrus fruit tree dataset.[Result]The results showed that the proposed method could effectively reconstruct the 3D structure of fruit tree.The average intersection over union(mIoU)of the improved network increased by 2.64 percentage points,and the intersection over union(IoU)of the fruit increased by 7.33 percentage points,verifying the practicality of this method in the scenario of smart orchards.[Conclusion]This study provides a new technical support for achieving intelligent management and automated fruit harvesting in orchards.关键词
果树重建/果实分割/神经辐射场/点云语义分割/智慧农业Key words
Fruit tree reconstruction/Fruit segmentation/Neural radiance field(NeRF)/Point cloud semantic segmentation/Smart agriculture分类
农业科技引用本文复制引用
杨达成,郭俊,杨景,张亚莉,兰玉彬..基于NeRF和改进RandLA-Net的果树三维重建与果实语义分割方法[J].华南农业大学学报,2025,46(4):528-537,10.基金项目
国家重点研发计划(2023YFD2000202) （2023YFD2000202）
