Improved RANSAC Point Cloud Spherical Target Detection and Parameter Estimation Method Based on Principal Curvature Constraint

Wu, Qinhua; Liu, Jiacheng; Gao, Can; Wang, Biao; Shen, Gaojian; Li, Zhiang

doi:10.3390/s22155850

Cited by 11 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The point clouds generated via multi-view reconstruction were dense,each maize tassel sample point cloud has more than 500,000 points,which cannot successfully run on the normally configured computer for deep learning training. Firstly,the random sample consensus (RANSAC) algorithm [ 42 ] is used, and the sample point cloud has been quickly down-sampled from 500,000 to 100,000 points. Down-sampling using the farthest sampling (FPS) [ 43 ] algorithm simplified the number of point clouds without destroying the point cloud distribution, therefore, which was used to further sample the sample point cloud from 100,000 to 40,000 points.…”

Section: Methodsmentioning

confidence: 99%

Three-dimensional branch segmentation and phenotype extraction of maize tassel based on deep learning

Zhang,

Wu,

Wen

et al. 2023

Plant Methods

View full text Add to dashboard Cite

Background The morphological structure phenotype of maize tassel plays an important role in plant growth, reproduction, and yield formation. It is an important step in the distinctness, uniformity, and stability (DUS) testing to obtain maize tassel phenotype traits. Plant organ segmentation can be achieved with high-precision and automated acquisition of maize tassel phenotype traits because of the advances in the point cloud deep learning method. However, this method requires a large number of data sets and is not robust to automatic segmentation of highly adherent organ components; thus, it should be combined with point cloud processing technology. Results An innovative method of incomplete annotation of point cloud data was proposed for easy development of the dataset of maize tassels,and an automatic maize tassel phenotype analysis system: MaizeTasselSeg was developed. The tip feature of point cloud is trained and learned based on PointNet + + network, and the tip point cloud of tassel branch was automatically segmented. Complete branch segmentation was realized based on the shortest path algorithm. The Intersection over Union (IoU), precision, and recall of the segmentation results were 96.29, 96.36, and 93.01, respectively. Six phenotypic traits related to morphological structure (branch count, branch length, branch angle, branch curvature, tassel volume, and dispersion) were automatically extracted from the segmentation point cloud. The squared correlation coefficients (R2) for branch length, branch angle, and branch count were 0.9897, 0.9317, and 0.9587, respectively. The root mean squared error (RMSE) for branch length, branch angle, and branch count were 0.529 cm, 4.516, and 0.875, respectively. Conclusion The proposed method provides an efficient scheme for high-throughput organ segmentation of maize tassels and can be used for the automatic extraction of phenotypic traits of maize tassel. In addition, the incomplete annotation approach provides a new idea for morphology-based plant segmentation.

show abstract

Section: Methodsmentioning

confidence: 99%

Three-dimensional branch segmentation and phenotype extraction of maize tassel based on deep learning

Zhang,

Wu,

Wen

et al. 2023

Plant Methods

View full text Add to dashboard Cite

show abstract

“…In order to further evaluate the construction error of the structure, the node welding ball model is fitted from the actual point cloud data of the structure based on the reverse modeling technology, and the spherical coordinates of the welding ball node are obtained. At present, the commonly used spherical fitting method is the RANSAC method [9][10] .…”

Section: Spherical Fitting Of Point Clouds Based On Reverse Modelingmentioning

confidence: 99%

Error analysis study of 3D laser scanning technology in large space grid structure measurement

Liao,

Mao,

Liu

et al. 2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

View full text Add to dashboard Cite

“…The radius constraint refers to using points larger than the minimum radius of the fitted stalk as leaf points. We treat the stem as a cylinder by default and fit it using the random consistent sampling method (RANSAC) [36], starting from the bottom of the stem and gradually traversing upward through all the stem points to obtain the minimum radius of the fitted cylinder, and presenting the points larger than the minimum radius to obtain a point cloud that is the leaf point cloud (Figure 5d). Finding the Path to the Highest Point of the Skeleton Firstly, the KdTree search mechanism is used to traverse the entire skeleton p cloud to find the root vertex of the stalks.…”

Section: Radius Constraintmentioning

confidence: 99%

A Method for Tomato Plant Stem and Leaf Segmentation and Phenotypic Extraction Based on Skeleton Extraction and Supervoxel Clustering

Wang,

Liu,

Yang

et al. 2024

Agronomy

View full text Add to dashboard Cite

To address the current problem of the difficulty of extracting the phenotypic parameters of tomato plants in a non-destructive and accurate way, we proposed a method of stem and leaf segmentation and phenotypic extraction of tomato plants based on skeleton extraction and supervoxel clustering. To carry out growth and cultivation experiments on tomato plants in a solar greenhouse, we obtained multi-view image sequences of the tomato plants to construct three-dimensional models of the plant. We used Laplace’s skeleton extraction algorithm to extract the skeleton of the point cloud after removing the noise points using a multi-filtering algorithm, and, based on the plant skeleton, searched for the highest point path, height constraints, and radius constraints to separate the stem from the leaf. At the same time, a supervoxel segmentation method based on Euclidean distance was used to segment each leaf. We extracted a total of six phenotypic parameters of the plant: height, stem diameter, leaf angle, leaf length, leaf width and leaf area, using the segmented organs, which are important for the phenotype. The results showed that the average accuracy, average recall and average F1 scores of the stem and leaf segmentation were 0.88, 0.80 and 0.84, and the segmentation indexes were better than the other four segmentation algorithms; the coefficients of determination between the measurement values of the phenotypic parameters and the real values were 0.97, 0.84, 0.88, 0.94, 0.92 and 0.93; and the root-mean-square errors were 2.17 cm, 0.346 cm, 5.65°, 3.18 cm, 2.99 cm and 8.79 cm2. The measurement values of the proposed method had a strong correlation with the actual values, which could satisfy the requirements of daily production and provide technical support for the extraction of high-throughput phenotypic parameters of tomato plants in solar greenhouses.

show abstract

Improved RANSAC Point Cloud Spherical Target Detection and Parameter Estimation Method Based on Principal Curvature Constraint

Cited by 11 publications

References 21 publications

Three-dimensional branch segmentation and phenotype extraction of maize tassel based on deep learning

Three-dimensional branch segmentation and phenotype extraction of maize tassel based on deep learning

Error analysis study of 3D laser scanning technology in large space grid structure measurement

A Method for Tomato Plant Stem and Leaf Segmentation and Phenotypic Extraction Based on Skeleton Extraction and Supervoxel Clustering

Contact Info

Product

Resources

About