scholarly journals Model-Based 3D Point Cloud Segmentation for Automated Selective Broccoli Harvesting

2019 ◽  
pp. 448-459
Author(s):  
Hector A. Montes ◽  
Grzegorz Cielniak ◽  
Tom Duckett
Keyword(s):  
Point Cloud ◽  
3D Point Cloud ◽  
Model Based ◽  
Point Cloud Segmentation

scholarly journals Model based 3D point cloud segmentation for automated selective broccoli harvesting

2019 ◽  
Author(s):  
Hector A Montes ◽  
Grzegorz Cielniak ◽  
Tom Duckett
Keyword(s):  
Point Cloud ◽  
3D Point Cloud ◽  
Model Based ◽  
Point Cloud Segmentation

scholarly journals Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Teng Miao ◽  
Weiliang Wen ◽  
Yinglun Li ◽  
Sheng Wu ◽  
Chao Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Training Dataset ◽  
Growth Stages ◽  
3D Point Cloud ◽  
Plant Structure ◽  
Data Annotation ◽  
Cloud Data ◽  
Point Cloud Segmentation ◽  
Different Growth Stages

Abstract Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4–10 minutes to segment a maize shoot and consumes 10–20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.

A novel 3D point cloud segmentation algorithm based on multi-resolution supervoxel and MGS

2021 ◽  
Vol 42 (22) ◽  
pp. 8492-8525
Author(s):  
Han Li ◽  
Yongmei Liu ◽  
Chaoguang Men ◽  
Yong Fang
Keyword(s):  
Point Cloud ◽  
Segmentation Algorithm ◽  
3D Point Cloud ◽  
Point Cloud Segmentation

scholarly journals Real-time 3D point cloud segmentation using Growing Neural Gas with Utility

2016 ◽  
Author(s):  
Yuichiro Toda ◽  
Zhaojie Ju ◽  
Hui Yu ◽  
Naoyuki Takesue ◽  
Kazuyoshi Wada ◽  
...  
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
3D Point Cloud ◽  
Growing Neural Gas ◽  
Neural Gas ◽  
Point Cloud Segmentation

A pothole detection method based on 3D point cloud segmentation

2020 ◽  
Author(s):  
Ying Du ◽  
Zuofeng Zhou ◽  
Qingquan Wu ◽  
Huimin Huang ◽  
Mingming Xu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Detection Method ◽  
3D Point Cloud ◽  
Point Cloud Segmentation ◽  
Pothole Detection

Real Time Volume Measurement of Logistics Cartons Through 3D Point Cloud Segmentation

2021 ◽  
pp. 324-335
Author(s):  
Wu Yan ◽  
Chen Xu ◽  
Hongmin Wu ◽  
Shuai Li ◽  
Xuefeng Zhou
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Volume Measurement ◽  
3D Point Cloud ◽  
Point Cloud Segmentation
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