scholarly journals 3D Point Cloud of Single Tree Branches and Leaves Semantic Segmentation Based on Modified PointNet Network

2021 ◽  
Vol 2074 (1) ◽  
pp. 012026
Author(s):  
Renpeng Liu ◽  
Lisheng Ren ◽  
Fang Wang
Keyword(s):  
Point Cloud ◽  
Semantic Segmentation ◽  
Normal Vector ◽  
3D Point Cloud ◽  
Tree Model ◽  
Single Tree ◽  
Point Cloud Segmentation ◽  
Segmentation Accuracy ◽  
Tree Branch ◽  
The Impact

Abstract Semantic segmentation of single tree 3D point cloud is one of the key technologies in building tree model. It plays an important role in tree skeleton extraction, tree pruning, tree model reconstruction and other fields. Because the area of a single leaf is smaller than that of the whole tree, the segmentation of branches and leaves is a challenging problem. In view of the above problems, this paper first migrates PointNet to the tree branch and leaf point cloud segmentation, and proposes an automatic segmentation method based on improved PointNet. According to the difference of normal direction between leaves and branches, the point cloud information of three dimensions coordinates, color and normal vector is input into the point feature space. In data processing, increase the number of each block data, so that the network can better learn features. MLP is added to the original PointNet network to improve the ability of extracting and learning local features. In addition, in the process of feature extraction, jump connection is added to realize feature reuse and make full use of different levels of features. The original 1×1 filter of PointNet is replaced by 3×1 filter to improve the segmentation accuracy of tree point cloud. The focus loss function focal loss is introduced into the field of 3D point cloud to reduce the impact of the imbalance of point cloud samples on the results. The results show that the improved method improves the accuracy of tree branch point cloud segmentation compared with the original PointNet for branch and leaf segmentation. The segmentation accuracy of structural elements of branches and leaves is more than 88%, and MIoU is 48%.

scholarly journals Point-cloud segmentation of individual trees in complex natural forest scenes based on a trunk-growth method

2021 ◽  
Author(s):  
Qianwei Liu ◽  
Weifeng Ma ◽  
Jianpeng Zhang ◽  
Yicheng Liu ◽  
Dongfan Xu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Plant Morphology ◽  
Natural Forest ◽  
Normal Vector ◽  
Natural Forests ◽  
Individual Tree ◽  
Single Tree ◽  
Point Cloud Segmentation ◽  
Individual Trees

AbstractForest resource management and ecological assessment have been recently supported by emerging technologies. Terrestrial laser scanning (TLS) is one that can be quickly and accurately used to obtain three-dimensional forest information, and create good representations of forest vertical structure. TLS data can be exploited for highly significant tasks, particularly the segmentation and information extraction for individual trees. However, the existing single-tree segmentation methods suffer from low segmentation accuracy and poor robustness, and hence do not lead to satisfactory results for natural forests in complex environments. In this paper, we propose a trunk-growth (TG) method for single-tree point-cloud segmentation, and apply this method to the natural forest scenes of Shangri-La City in Northwest Yunnan, China. First, the point normal vector and its Z-axis component are used as trunk-growth constraints. Then, the points surrounding the trunk are searched to account for regrowth. Finally, the nearest distributed branch and leaf points are used to complete the individual tree segmentation. The results show that the TG method can effectively segment individual trees with an average F-score of 0.96. The proposed method applies to many types of trees with various growth shapes, and can effectively identify shrubs and herbs in complex scenes of natural forests. The promising outcomes of the TG method demonstrate the key advantages of combining plant morphology theory and LiDAR technology for advancing and optimizing forestry systems.

scholarly journals Panicle-3D: Efficient Phenotyping Tool for Precise Semantic Segmentation of Rice Panicle Point Cloud

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Liang Gong ◽  
Xiaofeng Du ◽  
Kai Zhu ◽  
Ke Lin ◽  
Qiaojun Lou ◽  
...  
Keyword(s):  
Point Cloud ◽  
Network Models ◽  
Semantic Segmentation ◽  
Spatial Location ◽  
Point Clouds ◽  
Utilization Efficiency ◽  
Data Set ◽  
Cloud Data ◽  
Point Cloud Segmentation ◽  
Segmentation Accuracy

The automated measurement of crop phenotypic parameters is of great significance to the quantitative study of crop growth. The segmentation and classification of crop point cloud help to realize the automation of crop phenotypic parameter measurement. At present, crop spike-shaped point cloud segmentation has problems such as fewer samples, uneven distribution of point clouds, occlusion of stem and spike, disorderly arrangement of point clouds, and lack of targeted network models. The traditional clustering method can realize the segmentation of the plant organ point cloud with relatively independent spatial location, but the accuracy is not acceptable. This paper first builds a desktop-level point cloud scanning apparatus based on a structured-light projection module to facilitate the point cloud acquisition process. Then, the rice ear point cloud was collected, and the rice ear point cloud data set was made. In addition, data argumentation is used to improve sample utilization efficiency and training accuracy. Finally, a 3D point cloud convolutional neural network model called Panicle-3D was designed to achieve better segmentation accuracy. Specifically, the design of Panicle-3D is aimed at the multiscale characteristics of plant organs, combined with the structure of PointConv and long and short jumps, which accelerates the convergence speed of the network and reduces the loss of features in the process of point cloud downsampling. After comparison experiments, the segmentation accuracy of Panicle-3D reaches 93.4%, which is higher than PointNet. Panicle-3D is suitable for other similar crop point cloud segmentation tasks.

scholarly journals AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation

2020 ◽  
Author(s):  
Gege Zhang ◽  
Qinghua Ma ◽  
Licheng Jiao ◽  
Fang Liu ◽  
Qigong Sun
Keyword(s):  
Point Cloud ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
3D Point Cloud ◽  
Adversarial Learning ◽  
Adversarial Networks ◽  
Segmentation Accuracy ◽  
Segmentation Task

3D point cloud semantic segmentation has attracted wide attention with its extensive applications in autonomous driving, AR/VR, and robot sensing fields. However, in existing methods, each point in the segmentation results is predicted independently from each other. This property causes the non-contiguity of label sets in three-dimensional space and produces many noisy label points, which hinders the improvement of segmentation accuracy. To address this problem, we first extend adversarial learning to this task and propose a novel framework Attention Adversarial Networks (AttAN). With high-order correlations in label sets learned from the adversarial learning, segmentation network can predict labels closer to the real ones and correct noisy results. Moreover, we design an additive attention block for the segmentation network, which is used to automatically focus on regions critical to the segmentation task by learning the correlation between multi-scale features. Adversarial learning, which explores the underlying relationship between labels in high-dimensional space, opens up a new way in 3D point cloud semantic segmentation. Experimental results on ScanNet and S3DIS datasets show that this framework effectively improves the segmentation quality and outperforms other state-of-the-art methods.

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.

scholarly journals KVGCN: A KNN Searching and VLAD Combined Graph Convolutional Network for Point Cloud Segmentation

2021 ◽  
Vol 13 (5) ◽  
pp. 1003
Author(s):  
Nan Luo ◽  
Hongquan Yu ◽  
Zhenfeng Huo ◽  
Jinhui Liu ◽  
Quan Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Semantic Segmentation ◽  
K Nearest Neighbor ◽  
Topological Graph ◽  
Convolutional Network ◽  
Cloud Data ◽  
Nearest Neighbor Searching ◽  
Point Cloud Segmentation ◽  
Local Feature Extraction

Semantic segmentation of the sensed point cloud data plays a significant role in scene understanding and reconstruction, robot navigation, etc. This work presents a Graph Convolutional Network integrating K-Nearest Neighbor searching (KNN) and Vector of Locally Aggregated Descriptors (VLAD). KNN searching is utilized to construct the topological graph of each point and its neighbors. Then, we perform convolution on the edges of constructed graph to extract representative local features by multiple Multilayer Perceptions (MLPs). Afterwards, a trainable VLAD layer, NetVLAD, is embedded in the feature encoder to aggregate the local and global contextual features. The designed feature encoder is repeated for multiple times, and the extracted features are concatenated in a jump-connection style to strengthen the distinctiveness of features and thereby improve the segmentation. Experimental results on two datasets show that the proposed work settles the shortcoming of insufficient local feature extraction and promotes the accuracy (mIoU 60.9% and oAcc 87.4% for S3DIS) of semantic segmentation comparing to existing models.

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