scholarly journals Branch-Pipe: Improving Graph Skeletonization around Branch Points in 3D Point Clouds

2021 ◽  
Vol 13 (19) ◽  
pp. 3802
Author(s):  
Illia Ziamtsov ◽  
Kian Faizi ◽  
Saket Navlakha
Keyword(s):  
Branch Pipe ◽  
Point Clouds ◽  
Plant Phenotyping ◽  
Sampled Data ◽  
Branch Points ◽  
Tobacco Plants ◽  
Urban Scenes ◽  
3D Point Clouds ◽  
Large Numbers ◽  
Robust To Noise

Modern plant phenotyping requires tools that are robust to noise and missing data, while being able to efficiently process large numbers of plants. Here, we studied the skeletonization of plant architectures from 3D point clouds, which is critical for many downstream tasks, including analyses of plant shape, morphology, and branching angles. Specifically, we developed an algorithm to improve skeletonization at branch points (forks) by leveraging the geometric properties of cylinders around branch points. We tested this algorithm on a diverse set of high-resolution 3D point clouds of tomato and tobacco plants, grown in five environments and across multiple developmental timepoints. Compared to existing methods for 3D skeletonization, our method efficiently and more accurately estimated branching angles even in areas with noisy, missing, or non-uniformly sampled data. Our method is also applicable to inorganic datasets, such as scans of industrial pipes or urban scenes containing networks of complex cylindrical shapes.

scholarly journals Robust Surface Reconstruction of Plant Leaves from 3D Point Clouds

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ryuhei Ando ◽  
Yuko Ozasa ◽  
Wei Guo
Keyword(s):  
Surface Reconstruction ◽  
Leaf Surface ◽  
Imaging Techniques ◽  
Point Clouds ◽  
Plant Phenotyping ◽  
Reconstruction Method ◽  
Trade Off ◽  
3D Point Clouds ◽  
Leaf Surfaces ◽  
Conventional Methods

The automation of plant phenotyping using 3D imaging techniques is indispensable. However, conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction and the method’s robustness against noise and missing points. To mitigate this trade-off, we developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy by capturing two components of the leaf (the shape and distortion of that shape) separately using leaf-specific properties. This separation simplifies leaf surface reconstruction compared with conventional methods while increasing the robustness against noise and missing points. To evaluate the proposed method, we reconstructed the leaf surfaces from 3D point clouds of leaves acquired from two crop species (soybean and sugar beet) and compared the results with those of conventional methods. The result showed that the proposed method robustly reconstructed the leaf surfaces, despite the noise and missing points for two different leaf shapes. To evaluate the stability of the leaf surface reconstructions, we also calculated the leaf surface areas for 14 consecutive days of the target leaves. The result derived from the proposed method showed less variation of values and fewer outliers compared with the conventional methods.

scholarly journals Registration of spatio-temporal point clouds of plants for phenotyping

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247243
Author(s):  
Nived Chebrolu ◽  
Federico Magistri ◽  
Thomas Läbe ◽  
Cyrill Stachniss
Keyword(s):  
Plant Traits ◽  
Scale Up ◽  
Rigid Motion ◽  
Point Clouds ◽  
Plant Performance ◽  
Sensor Data ◽  
Plant Phenotyping ◽  
Compact Representation ◽  
3D Point Clouds ◽  
Over Time

Plant phenotyping is a central task in crop science and plant breeding. It involves measuring plant traits to describe the anatomy and physiology of plants and is used for deriving traits and evaluating plant performance. Traditional methods for phenotyping are often time-consuming operations involving substantial manual labor. The availability of 3D sensor data of plants obtained from laser scanners or modern depth cameras offers the potential to automate several of these phenotyping tasks. This automation can scale up the phenotyping measurements and evaluations that have to be performed to a larger number of plant samples and at a finer spatial and temporal resolution. In this paper, we investigate the problem of registering 3D point clouds of the plants over time and space. This means that we determine correspondences between point clouds of plants taken at different points in time and register them using a new, non-rigid registration approach. This approach has the potential to form the backbone for phenotyping applications aimed at tracking the traits of plants over time. The registration task involves finding data associations between measurements taken at different times while the plants grow and change their appearance, allowing 3D models taken at different points in time to be compared with each other. Registering plants over time is challenging due to its anisotropic growth, changing topology, and non-rigid motion in between the time of the measurements. Thus, we propose a novel approach that first extracts a compact representation of the plant in the form of a skeleton that encodes both topology and semantic information, and then use this skeletal structure to determine correspondences over time and drive the registration process. Through this approach, we can tackle the data association problem for the time-series point cloud data of plants effectively. We tested our approach on different datasets acquired over time and successfully registered the 3D plant point clouds recorded with a laser scanner. We demonstrate that our method allows for developing systems for automated temporal plant-trait analysis by tracking plant traits at an organ level.

scholarly journals GENERATION OF GROUND TRUTH DATASETS FOR THE ANALYSIS OF 3D POINT CLOUDS IN URBAN SCENES ACQUIRED VIA DIFFERENT SENSORS

2018 ◽  
Vol XLII-3 ◽  
pp. 2009-2015 ◽  
Author(s):  
Y. Xu ◽  
Z. Sun ◽  
R. Boerner ◽  
T. Koch ◽  
L. Hoegner ◽  
...  
Keyword(s):  
Point Cloud ◽  
Spatial Information ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Urban Scenes ◽  
3D Space ◽  
3D Point Clouds ◽  
Testing Site ◽  
Voxel Grid

In this work, we report a novel way of generating ground truth dataset for analyzing point cloud from different sensors and the validation of algorithms. Instead of directly labeling large amount of 3D points requiring time consuming manual work, a multi-resolution 3D voxel grid for the testing site is generated. Then, with the help of a set of basic labeled points from the reference dataset, we can generate a 3D labeled space of the entire testing site with different resolutions. Specifically, an octree-based voxel structure is applied to voxelize the annotated reference point cloud, by which all the points are organized by 3D grids of multi-resolutions. When automatically annotating the new testing point clouds, a voting based approach is adopted to the labeled points within multiple resolution voxels, in order to assign a semantic label to the 3D space represented by the voxel. Lastly, robust line- and plane-based fast registration methods are developed for aligning point clouds obtained via various sensors. Benefiting from the labeled 3D spatial information, we can easily create new annotated 3D point clouds of different sensors of the same scene directly by considering the corresponding labels of 3D space the points located, which would be convenient for the validation and evaluation of algorithms related to point cloud interpretation and semantic segmentation.

Plant 3D (P3D): a plant phenotyping toolkit for 3D point clouds

2020 ◽  
Vol 36 (12) ◽  
pp. 3949-3950
Author(s):  
Illia Ziamtsov ◽  
Saket Navlakha
Keyword(s):  
Plant Biomass ◽  
Point Clouds ◽  
Machine Learning Algorithms ◽  
Supplementary Information ◽  
Plant Phenotyping ◽  
Cloud Data ◽  
Graph Theoretic ◽  
3D Point Clouds ◽  
3D Architecture ◽  
Cloud Points

Abstract Motivation Developing methods to efficiently analyze 3D point cloud data of plant architectures remain challenging for many phenotyping applications. Here, we describe a tool that tackles four core phenotyping tasks: classification of cloud points into stem and lamina points, graph skeletonization of the stem points, segmentation of individual lamina and whole leaf labeling. These four tasks are critical for numerous downstream phenotyping goals, such as quantifying plant biomass, performing morphological analyses of plant shapes and uncovering genotype to phenotype relationships. The Plant 3D tool provides an intuitive graphical user interface, a fast 3D rendering engine for visualizing plants with millions of cloud points, and several graph-theoretic and machine-learning algorithms for 3D architecture analyses. Availability and implementation P3D is open-source and implemented in C++. Source code and Windows installer are freely available at https://github.com/iziamtso/P3D/. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256340
Author(s):  
David Schunck ◽  
Federico Magistri ◽  
Radu Alexandru Rosu ◽  
André Cornelißen ◽  
Nived Chebrolu ◽  
...  
Keyword(s):  
High Resolution ◽  
3D Reconstruction ◽  
Point Clouds ◽  
Training Data ◽  
Plant Phenotyping ◽  
Growth Stages ◽  
Plant Analysis ◽  
Rigid Registration ◽  
3D Point Clouds ◽  
Spatio Temporal

Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.

scholarly journals Clustering-Based Plane Segmentation Neural Network for Urban Scene Modeling

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8382
Author(s):  
Hongjae Lee ◽  
Jiyoung Jung
Keyword(s):  
Neural Network ◽  
Euclidean Distance ◽  
Point Clouds ◽  
Scene Modeling ◽  
Urban Scenes ◽  
3D Point Clouds ◽  
The Neural Network ◽  
Map Generation ◽  
Urban Scene ◽  
Cosine Distance

Urban scene modeling is a challenging but essential task for various applications, such as 3D map generation, city digitization, and AR/VR/metaverse applications. To model man-made structures, such as roads and buildings, which are the major components in general urban scenes, we present a clustering-based plane segmentation neural network using 3D point clouds, called hybrid K-means plane segmentation (HKPS). The proposed method segments unorganized 3D point clouds into planes by training the neural network to estimate the appropriate number of planes in the point cloud based on hybrid K-means clustering. We consider both the Euclidean distance and cosine distance to cluster nearby points in the same direction for better plane segmentation results. Our network does not require any labeled information for training. We evaluated the proposed method using the Virtual KITTI dataset and showed that our method outperforms conventional methods in plane segmentation. Our code is publicly available.

Deep Learning Method for Height Estimation of Sorghum in the Field Using LiDAR

2020 ◽  
Vol 2020 (14) ◽  
pp. 343-1-343-7
Author(s):  
Matthew Waliman ◽  
Avideh Zakhor
Keyword(s):  
Deep Learning ◽  
Estimation Error ◽  
Ground Truth ◽  
Point Clouds ◽  
Plant Phenotyping ◽  
Learning Method ◽  
Height Estimation ◽  
Ground Truth Data ◽  
Biomass Sorghum ◽  
3D Point Clouds

Automatic tools for plant phenotyping have received increased interest in recent years due to the need to understand the relationship between plant genotype and phenotype. Building upon our previous work, we present a robust, deep learning method to accurately estimate the height of biomass sorghum throughout the entirety of its growing season. We mount a vertically oriented LiDAR sensor onboard an agricultural robot to obtain 3D point clouds of the crop fields. From each of these 3D point clouds, we generate a height contour and density map corresponding to a single row of plants in the field. We then train a multiview neural network in order to estimate plant height. Our method is capable of accurately estimating height from emergence through canopy closure. We extensively validate our algorithm by performing several ground truthing campaigns on biomass sorghum. We have shown our proposed approach to achieve an absolute height estimation error of 7.47% using ground truth data obtained via conventional breeder methods on 2715 plots of sorghum with varying genetic strains and treatments.

scholarly journals EVALUATION AND OPTIMISATION OF CROWD-BASED COLLECTION OF TREES FROM 3D POINT CLOUDS

2020 ◽  
Vol V-4-2020 ◽  
pp. 49-56
Author(s):  
V. Walter ◽  
M. Kölle ◽  
Y. Yin
Keyword(s):  
Reference Data ◽  
Point Clouds ◽  
Third Parties ◽  
The Internet ◽  
Web Interface ◽  
3D Point Clouds ◽  
Large Numbers

Abstract. The term "Crowdsourcing" goes back to Jeff Howe (Howe, 2006) and represents a neologism of the words "crowd" and "outsourcing". Unlike outsourcing, where companies outsource certain tasks to known third parties, crowdsourcing outsources tasks to unknown workers (crowdworkers) on the Internet. This allows companies to access large numbers of workers who would otherwise not be available. In this paper, we will discuss an approach for the crowd-based collection of trees by means of minimum bounding cylinders from 3D point clouds. We will demonstrate the used web-interface and compare the results with reference data. To improve the quality of the results, we collect the data not only once but multiple times. This enables us to implement a so-called “Wisdom of the Crowd” approach where we can identify automatically outliers and derive integrated cylinders. We will show in this paper that this approach increases significantly the quality of the results.

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