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research-article

Analyzing growing plants from 4D point cloud data

Authors:

Niloy J. Mitra,

Daniel Chamovitz,

Daniel Cohen-Or,

Baoquan ChenAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 32, Issue 6

Article No.: 157, Pages 1 - 10

https://doi.org/10.1145/2508363.2508368

Published: 01 November 2013 Publication History

Abstract

Studying growth and development of plants is of central importance in botany. Current quantitative are either limited to tedious and sparse manual measurements, or coarse image-based 2D measurements. Availability of cheap and portable 3D acquisition devices has the potential to automate this process and easily provide scientists with volumes of accurate data, at a scale much beyond the realms of existing methods. However, during their development, plants grow new parts (e.g., vegetative buds) and bifurcate to different components --- violating the central incompressibility assumption made by existing acquisition algorithms, which makes these algorithms unsuited for analyzing growth. We introduce a framework to study plant growth, particularly focusing on accurate localization and tracking topological events like budding and bifurcation. This is achieved by a novel forward-backward analysis, wherein we track robustly detected plant components back in time to ensure correct spatio-temporal event detection using a locally adapting threshold. We evaluate our approach on several groups of time lapse scans, often ranging from days to weeks, on a diverse set of plant species and use the results to animate static virtual plants or directly attach them to physical simulators.

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Cited By

Li DAhmed FWang Z(2025)3D-NOD: 3D New Organ Detection in Plant Growth by a Spatiotemporal Point Cloud Deep Segmentation FrameworkPlant Phenomics10.1016/j.plaphe.2025.100002(100002)Online publication date: Feb-2025
https://doi.org/10.1016/j.plaphe.2025.100002
Song HWen WWu SGuo X(2025)Comprehensive review on 3D point cloud segmentation in plantsArtificial Intelligence in Agriculture10.1016/j.aiia.2025.01.006Online publication date: Jan-2025
https://doi.org/10.1016/j.aiia.2025.01.006
Dutta SRussig BGumhold S(2025)Enhanced Plant Phenotyping Through Spatio-Temporal Point Cloud RegistrationAdvances in Computer Graphics10.1007/978-3-031-81806-6_27(358-370)Online publication date: 27-Feb-2025
https://doi.org/10.1007/978-3-031-81806-6_27
Show More Cited By

Index Terms

Analyzing growing plants from 4D point cloud data
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Video segmentation
  2. Computer graphics
    1. Shape modeling
2. Theory of computation
  1. Randomness, geometry and discrete structures

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 32, Issue 6

November 2013

671 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2508363

Issue’s Table of Contents

Copyright © 2013 ACM.

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Publication History

Published: 01 November 2013

Published in TOG Volume 32, Issue 6

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Funding Sources

Guangdong Science and Technology Program
Shenzhen Innovation Program
National Natural Science Foundation of China
Ministry of Science and Technology of the People's Republic of China
Seventh Framework Programme
European Research Council
Israel Science Foundation

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Cited By

Li DAhmed FWang Z(2025)3D-NOD: 3D New Organ Detection in Plant Growth by a Spatiotemporal Point Cloud Deep Segmentation FrameworkPlant Phenomics10.1016/j.plaphe.2025.100002(100002)Online publication date: Feb-2025
https://doi.org/10.1016/j.plaphe.2025.100002
Song HWen WWu SGuo X(2025)Comprehensive review on 3D point cloud segmentation in plantsArtificial Intelligence in Agriculture10.1016/j.aiia.2025.01.006Online publication date: Jan-2025
https://doi.org/10.1016/j.aiia.2025.01.006
Dutta SRussig BGumhold S(2025)Enhanced Plant Phenotyping Through Spatio-Temporal Point Cloud RegistrationAdvances in Computer Graphics10.1007/978-3-031-81806-6_27(358-370)Online publication date: 27-Feb-2025
https://doi.org/10.1007/978-3-031-81806-6_27
Xie KZhu JRen HWang YYang WChen GLin CZhai R(2024)Delving into the Potential of Deep Learning Algorithms for Point Cloud Segmentation at Organ Level in Plant PhenotypingRemote Sensing10.3390/rs1617329016:17(3290)Online publication date: 4-Sep-2024
https://doi.org/10.3390/rs16173290
Rodriguez-Sanchez JSnider JJohnsen KLi C(2024)Cotton morphological traits tracking through spatiotemporal registration of terrestrial laser scanning time-series dataFrontiers in Plant Science10.3389/fpls.2024.143612015Online publication date: 1-Aug-2024
https://doi.org/10.3389/fpls.2024.1436120
Li BSchwarz NPałubicki WPirk SBenes B(2024)Interactive Invigoration: Volumetric Modeling of Trees with StrandsACM Transactions on Graphics10.1145/365820643:4(1-13)Online publication date: 19-Jul-2024
https://doi.org/10.1145/3658206
Wan TDu SZhang QQi YHuang CZeng W(2024)Robust colored point cloud alignment based on L*a*b* guided and Cauchy kernelComputational Intelligence10.1111/coin.1265740:3Online publication date: 17-Jun-2024
https://doi.org/10.1111/coin.12657
Li JLiu JHuang Q(2023)PointDMM: A Deep-Learning-Based Semantic Segmentation Method for Point Clouds in Complex Forest EnvironmentsForests10.3390/f1412227614:12(2276)Online publication date: 21-Nov-2023
https://doi.org/10.3390/f14122276
Das ADas Choudhury SDas ASamal AAwada T(2023)EmergeNet: A novel deep-learning based ensemble segmentation model for emergence timing detection of coleoptileFrontiers in Plant Science10.3389/fpls.2023.108477814Online publication date: 3-Feb-2023
https://doi.org/10.3389/fpls.2023.1084778
Fouché GArgelaguet FFaure EKervrann C(2023)Immersive and interactive visualization of 3D spatio-temporal data using a space time hypercube: Application to cell division and morphogenesis analysisFrontiers in Bioinformatics10.3389/fbinf.2023.9989913Online publication date: 8-Mar-2023
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