基于SLAM與神經(jīng)輻射場(chǎng)的柑橘幼苗三維重建方法

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中圖分類(lèi)號(hào)：S24；S666 文獻(xiàn)標(biāo)志碼：A 文章編號(hào)：1001-411X（2025）03-0429-10

3D reconstruction of citrus seedlings based on SLAM and NeRF

GUO Jun1， YANG Dacheng1，MO Zhenjie1，LAN Yubin2， ZHANG Yali1，2 （1 College ofEngineering，South China Agricultural University，Guangzhou5064，China;2National CenterforInteatioal Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology，Guangzhou510642，China）

Abstract： 【Objective】Aiming at the problem that it is difficult to obtain the accurate 3D point cloud of citrus seedlings and their 3D phenotypic parameters to characterize the state of seedlingswith the existing 3D reconstruction techniques，this paper proposes a method based on the simultaneous localization and mapping （SLAM）and neural radiance fields （NeRF） for 3D reconstruction of citrus seedlings. 【Method】 One-year old citrus seedlings were taken as the research object.Firstly，a depth sensor was used to capture the RGB map and depth map of the citrus seedling. Secondly， SLAM was employed to obtain the poses of the depth sensor in each frame of the image.Then， NeRF was trained for citrus seedlings，and the multi-view images with attached positional pose were fed into the multilayer erceptron （MLP）. Finally，through supervised training with volume rendering，a high-precision 3D realistic point cloud model of citrus seedlings was reconstructed. 【Result】The 3D modelofcitrus seedlings reconstructed by this method was highly realistic in terms ofcolor and texture， with clear contours and distinct layers，and had real-world level accuracy. Based on this model， the 3D phenotypic parameters of citrus seedlings could be effectively extracted with the accuracy of 9 7 . 9 4 % for plant height， 9 3 . 9 5 % for breadth length， 9 4 . 1 1 % for breadth width and 9 7 . 6 2 % for stem thickness. 【Conclusion】 This study helps to accelerate the selection and nursery process of excelent citrus seedlings and provides a technical support for the sustainable development of the citrus industry.

Key Words： Citrus seedling; Plant 3D phenotype; 3D reconstruction; Neural radiance fields （NeRF）; Simultaneous localization and mapping （SLAM）; Deep learning

廣東是全國(guó)傳統(tǒng)的柑橘優(yōu)勢(shì)產(chǎn)區(qū)之一，柑橘種植歷史悠久，年產(chǎn)量逐年上升。（剩余13301字）