[1]张雪景,樊惠超,孙佳明,等.基于计算机视觉的植物三维重建方法综述[J].江苏农业科学,2026,54(12):21-29.
　Zhang Xuejing,et al.3D reconstruction methods for plants based on computer vision：a review[J].Jiangsu Agricultural Sciences,2026,54(12):21-29.
点击复制

基于计算机视觉的植物三维重建方法综述()

《江苏农业科学》[ISSN:1002-1302/CN:32-1214/S]

卷:
第54卷
期数:
2026年第12期
页码:
21-29
栏目:
专论与综述
出版日期:
2026-06-20

文章信息/Info

Title:
3D reconstruction methods for plants based on computer vision：a review
作者:
张雪景; 樊惠超; 孙佳明; 刘金; 桑士舟
廊坊市农林科学院农业信息研究所，河北廊坊 065000
Author(s):
Zhang Xuejing; et al
关键词:
计算机视觉; 三维重建; 植物表型; 传感技术
Keywords:
-
分类号:
TP391.41
DOI:
-
文献标志码:
A
摘要:
随着计算机视觉与各种先进传感器技术的不断发展，三维重建技术在各行业的应用变得更加广泛。在农业领域，对植物进行三维重建可以将其可视化、数字化，便于存储在计算机当中，可以实现表型参数信息的获取，判断植株长势，预测病虫害以及产量等。介绍并分析近年来国内外相关的植物三维重建技术，首先从主动式三维重建技术开始，分析结构光法三维重建、飞行时间法三维重建以及激光雷达法三维重建；其次，分析被动式三维重建的中的单目视觉、双目视觉以及多目视觉；最后分析近年来愈加火热的基于深度学习的三维重建技术。通过分析以上技术实例之间的区别与联系，对上述技术的原理与优缺点进行总结与分类，并进一步分析三维重建技术在农业植物领域的优势与挑战，最后面对三维重建技术的现状，对农业植物三维重建技术进行总结与展望。
Abstract:
-

参考文献/References:

［1］仇瑞承，魏爽，张漫，等. 作物表型组学测量方法综述［J］. 中国农业文摘(农业工程)，2019，31(1)：23-36，55.
［2］Li L，Zhang Q，Huang D. A review of imaging techniques for plant phenotyping［J］. Sensors，2014，14(11)：20078-20111.
［3］Yang W N，Feng H，Zhang X H，et al. Crop phenomics and high-throughput phenotyping：past decades，current challenges，and future perspectives［J］. Molecular Plant，2020，13(2)：187-214.
［4］Harandi N，Vandenberghe B，Vankerschaver J，et al. How to make sense of 3D representations for plant phenotyping：a compendium of processing and analysis techniques［J］. Plant Methods，2023，19(1)：60.
［5］Ward B，Brien C，Oakey H，et al. High-throughput 3D modelling to dissect the genetic control of leaf elongation in barley (Hordeum vulgare)［J］. The Plant Journal，2019，98(3)：555-570.
［6］Gao T，Zhu F Y，Paul P，et al. Novel 3D imaging systems for high-throughput phenotyping of plants［J］. Remote Sensing，2021，13(11)：2113.
［7］郭庆华，杨维才，吴芳芳，等. 高通量作物表型监测：育种和精准农业发展的加速器［J］. 中国科学院院刊，2018，33(9)：940-946.
［8］Zhao C J，Zhang Y，Du J J，et al. Crop phenomics：current status and perspectives［J］. Frontiers in Plant Science，2019，10：714.
［9］Yu J，Yin W B，Hu Z Y，et al. 3D reconstruction for multi-view objects［J］. Computers and Electrical Engineering，2023，106：108567.
［10］周静静，郭新宇，吴升，等. 基于多视角图像的植物三维重建研究进展［J］. 中国农业科技导报，2019，21(2)：9-18.
［11］Massot-Campos M，Oliver-Codina G. Optical sensors and methods for underwater 3D reconstruction［J］. Sensors，2015，15(12)：31525-31557.
［12］Zhang J，Luo B，Su X，et al. A convenient 3D reconstruction model based on parallel-axis structured light system［J］. Optics and Lasers in Engineering，2021，138：106366.
［13］Jung S，Lee Y S，Lee Y，et al. 3D reconstruction using 3D registration-based ToF-stereo fusion［J］. Sensors，2022，22(21)：8369.
［14］Zhou X Z，Wu Y F，Meng H W，et al. Three-dimensional reconstruction of the furrow shape in orchards using a low-cost lidar［J］. Frontiers in Sustainable Food Systems，2023，7：1201994.
［15］Zuo C，Zhang X L，Hu Y，et al. Has 3D finally come of age？：an introduction to 3D structured-light sensor［J］. Infrared and Laser Engineering，2020，49(3)：0303001.
［16］吕深圳. 基于数字光栅投影技术的三维面型测量研究［D］. 长春：中国科学院大学(中国科学院长春光学精密机械与物理研究所)，2021：4-6.
［17］Hennad A，Cockett P，McLauchlan L，et al. Characterization of irregularly-shaped objects using 3D structured light scanning［C］//2019 International Conference on Computational Science and Computational Intelligence (CSCI). Las Vegas，NV，USA：IEEE，2019：600-605.
［18］Qi Z S，Wang Z. Highlight removal based on the regional-projection fringe projection method［J］. Optical Engineering，2018，57(4)：1.
［19］Lin H，Gao J，Mei Q，et al. Adaptive digital fringe projection technique for high dynamic range three-dimensional shape measurement［J］. Optics Express，2016，24(7)：7703.
［20］束宏伟，王玉伟，饶元，等. 融合三维结构光和叶绿素荧光的植株光合表型成像系统［J］. 智慧农业(中英文)，2024，6(1)：63-75.
［21］陈旭佳，彭羽萌，俞盛旗，等. 基于双目结构光三维重建的类球形水果外形指标测量［J］. 农业工程学报，2024，40(20)：187-194.
［22］Sansoni G，Trebeschi M，Docchio F. State-of-the-art and applications of 3D imaging sensors in industry，cultural heritage，medicine，and criminal investigation［J］. Sensors，2009，9(1)：568-601.
［23］Chua S Y，Wang X，Guo N，et al. Improving three-dimensional (3D) range gated reconstruction through time-of-flight (TOF) imaging analysis［J］. Journal of the European Optical Society-Rapid Publications，2016，11：16015.
［24］Zeng G L，Li Y，Huang Q. Analytic time-of-flight positron emission tomography reconstruction：three-dimensional case［J］. Visual Computing for Industry，Biomedicine，and Art，2020，3(1)：5.
［25］Feng T，Yao S L，Xi C，et al. Deep learning-based image reconstruction for TOF PET with DIRECT data partitioning format［J］. Physics in Medicine & Biology，2021，66(16)：165007.
［26］Antholzer S，Wolf C，Sandbichler M，et al. Compressive time-of-flight 3D imaging using block-structured sensing matrices［J］. Inverse Problems，2019，35(4)：045004.
［27］Li Y S，Matej S，Karp J S. Practical joint reconstruction of activity and attenuation with autonomous scaling for time-of-flight PET［J］. Physics in Medicine & Biology，2020，65(23)：235037.
［28］Xie W J，Wei S，Yang D Y. Morphological measurement for carrot based on three-dimensional reconstruction with a ToF sensor［J］. Postharvest Biology and Technology，2023，197：112216.
［29］Liu Y D，Yuan H B，Zhao X，et al. Fast reconstruction method of three-dimension model based on dual RGB-D cameras for peanut plant［J］. Plant Methods，2023，19(1)：17.
［30］Pan Y，Han Y，Wang L，et al. 3D reconstruction of ground crops based on airborne LiDAR technology［J］. IFAC-PapersOnLine，2019，52(24)：35-40.
［31］Paulus S. Measuring crops in 3D：using geometry for plant phenotyping［J］. Plant Methods，2019，15(1)：103.
［32］Jo Y H，Hong S. Three-dimensional digital documentation of cultural heritage site based on the convergence of terrestrial laser scanning and unmanned aerial vehicle photogrammetry［J］. ISPRS International Journal of Geo-Information，2019，8(2)：53.
［33］Yeong D J，Velasco-Hernandez G，Barry J，et al. Sensor and sensor fusion technology in autonomous vehicles：a review［J］. Sensors，2021，21(6)：2140.
［34］Wu J W，Xue X Y，Zhang S C，et al. Plant 3D reconstruction based on LiDAR and multi-view sequence images［J］. International Journal of Precision Agricultural Aviation，2018，1(1)：37-43.
［35］Saha K K，Tsoulias N，Weltzien C，et al. Estimation of vegetative growth in strawberry plants using mobile LiDAR laser scanner［J］. Horticulturae，2022，8(2)：90.
［36］Li S，Miao Y L，Li H，et al. RTMR-LOAM：real-time maize 3D reconstruction based on lidar odometry and mapping［J］. Computers and Electronics in Agriculture，2025，230：109820.
［37］Moons T，van Gool L，Vergauwen M. 3D reconstruction from multiple images part 1：principles［J］. Foundations and Trends in Computer Graphics and Vision，2010，4(4)：287-404.
［38］Ding H Z，Zhang B H，Zhou J，et al. Recent developments and applications of simultaneous localization and mapping in agriculture［J］. Journal of Field Robotics，2022，39(6)：956-983.
［39］Iglhaut J，Cabo C，Puliti S，et al. Structure from motion photogrammetry in forestry：a review［J］. Current Forestry Reports，2019，5(3)：155-168.
［40］Schonberger J L，Frahm J M. Structure-from-motion revisited［C］//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas，NV，USA：IEEE，2016：4104-4113.
［41］Li Y C，Liu J Y，Zhang B，et al. Three-dimensional reconstruction and phenotype measurement of maize seedlings based on multi-view image sequences［J］. Frontiers in Plant Science，2022，13：974339.
［42］Rai A，Peddinti P R T，Kim B，et al. Phenotypic trait monitoring of Victoria amazonica plants using unmanned aerial vehicles［J］. Journal of the Indian Society of Remote Sensing，2025，53(7)：2311-2329.
［43］Huo G Y，Wu Z Y，Li J B，et al. Underwater target detection and 3D reconstruction system based on binocular vision［J］. Sensors，2018，18(10)：3570.
［44］Tian X，Liu R，Wang Z Y，et al. High quality 3D reconstruction based on fusion of polarization imaging and binocular stereo vision［J］. Information Fusion，2022，77：19-28.
［45］Sun J H，Zhang Y，and Cheng X Q. A high precision 3D reconstruction method for bend tube axis based on binocular stereo vision［J］. Optics Express，2019，27(3)：2292-2304.
［46］Ye J，Xia G S，Liu F，et al. 3D reconstruction of line-structured light based on binocular vision calibration rotary axis［J］. Applied Optics，2020，59(27)：8272.
［47］Wang L Y，Miao Y L，Han Y X，et al. Extraction of 3D distribution of potato plant CWSI based on thermal infrared image and binocular stereovision system［J］. Frontiers in Plant Science，2023，13：1104390.
［48］Zhang Y W，Gu J，Rao T，et al. A shape reconstruction and measurement method for spherical hedges using binocular vision［J］. Frontiers in Plant Science，2022，13：849821.
［49］韩雨坤，潘翀，王文涛，等. 基于多目立体视觉和神经网络标定的表面形貌测量方法研究［J］. 实验流体力学，2021，35(6)：44-51.
［50］Chen M Y，Tang Y C，Zou X J，et al. Three-dimensional perception of orchard banana central stock enhanced by adaptive multi-vision technology［J］. Computers and Electronics in Agriculture，2020，174：105508.
［51］Li M X，Li T M and Jiang Y. Marker displacement method used in vision-based tactile sensors-from 2D to 3D：a review［J］. IEEE Sensors Journal，2023，23(8)：8042-8059.
［52］Wang F Y，Ma X D，Liu M，et al. Three-dimensional reconstruction of soybean canopy based on multivision technology for calculation of phenotypic traits［J］. Agronomy，2022，12(3)：692.
［53］van den Heuvel F A. 3D reconstruction from a single image using geometric constraints［J］. ISPRS Journal of Photogrammetry and Remote Sensing，1998，53(6)：354-368.
［54］Fu K，Peng J S，He Q W，et al. Single image 3D object reconstruction based on deep learning：a review［J］. Multimedia Tools and Applications，2021，80(1)：463-498.
［55］Anwar S，Barnes N，Petersson L. A systematic evaluation：fine-grained CNN vs.traditional CNN classifiers［J］. Electronics，2023，12(23)：4877.
［56］Cho J，Yoon K. Conditional activation GAN：improved auxiliary classifier GAN［J］. IEEE Access，2020，8：216729-216740.
［57］Ma J，Aung Y M，Manzie T，et al. Clinical application of 3-dimensional tissue volume assessment using CloudCompare［J］. British Journal of Oral and Maxillofacial Surgery，2023，61(8)：567-570.
［58］Li J S，Wang Y，Zheng L H，et al. Towards end-to-end deep RNN based networks to precisely regress of the lettuce plant height by single perspective sparse 3D point cloud［J］. Expert Systems with Applications，2023，229：120497.
［59］Lu Y W，Wang Y X，Chen Z J，et al. 3D plant root system reconstruction based on fusion of deep structure-from-motion and IMU［J］. Multimedia Tools and Applications，2021，80(11)：17315-17331.
［60］Hartley Z K J，Jackson A S，Pound M，et al. GANana：unsupervised domain adaptation for volumetric regression of fruit［J］. Plant Phenomics，2021，2021：9874597.
［61］Du R M，Ma Z H，Xie P Y，et al. PST：plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage［J］. ISPRS Journal of Photogrammetry and Remote Sensing，2023，195：380-392.
［62］Wu S X，Hu C H，Tian B Y，et al. A 3D reconstruction platform for complex plants using OB-NeRF［J］. Frontiers in Plant Science，2025，16：1449626.
［63］Yu S Q，Yan X J，Jia T Z，et al. Binocular structured light-based 3D reconstruction for morphological measurements of apples［J］. Postharvest Biology and Technology，2024，213：112952.
［64］Huang X，Zheng S Y，Zhu N N. High-throughput legume seed phenotyping using a handheld 3D laser scanner［J］. Remote Sensing，2022，14(2)：431.
［65］Li Y J，Feng Q C，Lin J W，et al. 3D locating system for pests laser control based on multi-constraint stereo matching［J］. Agriculture，2022，12(6)：766.
［66］Bhadra S，Sagan V，Skobalski J，et al. End-to-end 3D CNN for plot-scale soybean yield prediction using multitemporal UAV-based RGB images［J］. Precision Agriculture，2024，25(2)：834-864.

相似文献/References:

[1]朱家骥,朱伟兴.基于星状骨架模型的猪步态分析[J].江苏农业科学,2015,43(12):453.
　Zhu Jiaji,et al.Analysis of pigs gaits based on star shaped frame model[J].Jiangsu Agricultural Sciences,2015,43(12):453.
[2]陈桂珍,龚声蓉.计算机视觉及模式识别技术在农业生产领域的应用[J].江苏农业科学,2015,43(08):409.
　Chen Guizhen,et al.Application of computer vision and pattern recognition in agricultural production field[J].Jiangsu Agricultural Sciences,2015,43(12):409.
[3]劳东青,陈立平,邬欢欢,等.基于计算机视觉的枣叶含水率估算模型[J].江苏农业科学,2015,43(04):384.
　Lao Dongqing,et al.Study on jujube leaf water content estimation model based on computer vision[J].Jiangsu Agricultural Sciences,2015,43(12):384.
[4]王爱新,李春友,张喆.基于计算机视觉的农业图像害虫定位检测算法[J].江苏农业科学,2016,44(07):361.
　Wang Aixin,et al.Agricultural image pest location detection algorithm based on computer vision[J].Jiangsu Agricultural Sciences,2016,44(12):361.
[5]祁卫宇,王传宇,郭新宇.基于计算机视觉的植物行为感知研究综述[J].江苏农业科学,2017,45(06):20.
　Qi Weiyu,et al.Study on plant behavior perception based on computer vision： a review[J].Jiangsu Agricultural Sciences,2017,45(12):20.
[6]邢志中,张海东,王孟,等.基于计算机视觉和神经网络的鸡蛋新鲜度检测[J].江苏农业科学,2017,45(11):160.
　Xing Zhizhong,et al.Detection of egg freshness based on computer vision detection and neural network[J].Jiangsu Agricultural Sciences,2017,45(12):160.
[7]陈彩文,杜永贵,周超,等.基于支持向量机的鱼群摄食行为识别技术[J].江苏农业科学,2018,46(07):226.
　Chen Caiwen,et al.Study on fish feeding behavior recognition technology based on support vector machine[J].Jiangsu Agricultural Sciences,2018,46(12):226.
[8]刘祖鹏.基于优化PCNN模型的黄瓜叶片病斑提取方法[J].江苏农业科学,2018,46(18):216.
　Liu Zupeng.A cucumber leaf lesion extraction method based on optimized PCNN mode[J].Jiangsu Agricultural Sciences,2018,46(12):216.
[9]童阳,艾施荣,吴瑞梅,等.茶叶外形感官品质的计算机视觉分级研究[J].江苏农业科学,2019,47(05):170.
　Tong Yang,et al.Sensory evaluation of tea appearance using computer vision classification[J].Jiangsu Agricultural Sciences,2019,47(12):170.
[10]张重阳,陈明.基于计算机视觉的鱼类摄食行为研究现状及展望[J].江苏农业科学,2020,48(24):31.
　Zhang Chongyang,et al.Research status and outlook of fish feeding behavior based on computer vision[J].Jiangsu Agricultural Sciences,2020,48(12):31.

备注/Memo

备注/Memo:
收稿日期：2025-07-01
作者简介：张雪景(1997—)，男，河北廊坊人，硕士，农业技术员，研究方向为植物机器视觉与表型获取。E-mail：1044260564@qq.com。
通信作者：桑士舟，硕士，高级农艺师，从事农业信息化研究与应用、作物育种与配套栽培技术等科研工作。E-mail：195608183@qq.com。
更新日期/Last Update: 2026-06-20