An Automatic Method for Tree Species Point Cloud Segmentation Based on Deep Learning

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.

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Vision-based volumetric measurements via deep learning-based point cloud segmentation for material management in jobsites

Automation in Construction ◽

10.1016/j.autcon.2020.103430 ◽

2021 ◽

Vol 121 ◽

pp. 103430

Author(s):

Mirsalar Kamari ◽

Youngjib Ham

Keyword(s):

Deep Learning ◽

Point Cloud ◽

Material Management ◽

Point Cloud Segmentation ◽

Volumetric Measurements

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Automatic organ-level point cloud segmentation of maize shoots by integrating high-throughput data acquisition and deep learning

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2022.106702 ◽

2022 ◽

Vol 193 ◽

pp. 106702

Author(s):

Yinglun Li ◽

Weiliang Wen ◽

Teng Miao ◽

Sheng Wu ◽

Zetao Yu ◽

...

Keyword(s):

Deep Learning ◽

Data Acquisition ◽

High Throughput ◽

Point Cloud ◽

High Throughput Data ◽

Point Cloud Segmentation ◽

Organ Level

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Research on a Single-Tree Point Cloud Segmentation Method Based on UAV Tilt Photography and Deep Learning Algorithm

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2020.3008918 ◽

2020 ◽

Vol 13 ◽

pp. 4111-4120

Author(s):

Xiaoyu Hu ◽

Dan Li

Keyword(s):

Deep Learning ◽

Point Cloud ◽

Learning Algorithm ◽

Segmentation Method ◽

Single Tree ◽

Deep Learning Algorithm ◽

Point Cloud Segmentation

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PIG-Net: Inception based deep learning architecture for 3D point cloud segmentation

Computers & Graphics ◽

10.1016/j.cag.2021.01.004 ◽

2021 ◽

Vol 95 ◽

pp. 13-22

Author(s):

Sindhu Hegde ◽

Shankar Gangisetty

Keyword(s):

Deep Learning ◽

Point Cloud ◽

3D Point Cloud ◽

Point Cloud Segmentation

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Classification of Typical Tree Species in Laser Point Cloud Based on Deep Learning

Remote Sensing ◽

10.3390/rs13234750 ◽

2021 ◽

Vol 13 (23) ◽

pp. 4750

Author(s):

Jianchang Chen ◽

Yiming Chen ◽

Zhengjun Liu

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Tree Species ◽

Classification Accuracy ◽

Point Cloud ◽

Laser Scanning ◽

Species Classification ◽

Tree Species Classification ◽

Network Mapping

We propose the Point Cloud Tree Species Classification Network (PCTSCN) to overcome challenges in classifying tree species from laser data with deep learning methods. The network is mainly composed of two parts: a sampling component in the early stage and a feature extraction component in the later stage. We used geometric sampling to extract regions with local features from the tree contours since these tend to be species-specific. Then we used an improved Farthest Point Sampling method to extract the features from a global perspective. We input the intensity of the tree point cloud as a dimensional feature and spatial information into the neural network and mapped it to higher dimensions for feature extraction. We used the data obtained by Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle Laser Scanning (UAVLS) to conduct tree species classification experiments of white birch and larch. The experimental results showed that in both the TLS and UAVLS datasets, the input tree point cloud density and the highest feature dimensionality of the mapping had an impact on the classification accuracy of the tree species. When the single tree sample obtained by TLS consisted of 1024 points and the highest dimension of the network mapping was 512, the classification accuracy of the trained model reached 96%. For the individual tree samples obtained by UAVLS, which consisted of 2048 points and had the highest dimension of the network mapping of 1024, the classification accuracy of the trained model reached 92%. TLS data tree species classification accuracy of PCTSCN was improved by 2–9% compared with other models using the same point density, amount of data and highest feature dimension. The classification accuracy of tree species obtained by UAVLS was up to 8% higher. We propose PCTSCN to provide a new strategy for the intelligent classification of forest tree species.

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Benchmarking Deep Learning Models on Point Cloud Segmentation

10.3233/faia210152 ◽

2021 ◽

Author(s):

Thanasis Zoumpekas ◽

Guillem Molina ◽

Maria Salamó ◽

Anna Puig

Keyword(s):

Deep Learning ◽

Point Cloud ◽

Performance Metrics ◽

Point Clouds ◽

Learning Models ◽

Cloud Data ◽

Point Cloud Segmentation ◽

Segmentation Analysis ◽

Intelligent Analysis ◽

Segmentation Task

Point clouds are currently used for a variety of applications, such as detection tasks in medical and geological domains. Intelligent analysis of point clouds is considered a highly computationally demanding and challenging task, especially the segmentation task among the points. Although numerous deep learning models have recently been proposed to segment point cloud data, there is no clear instruction of which exactly neural network to utilize and then incorporate into a system dealing with point cloud segmentation analysis. Besides, the majority of the developed models emphasize more on the accuracy rather than the efficiency, in order to achieve great results. Consequently, the training, validation and testing phases of the models require a great number of processing hours and a huge amount of memory. These high computational requirements are commonly difficult to deal with for many users. In this article, we analyse five state-of-the-art deep learning models for part segmentation task and give meaningful insights into the utilization of each one. We advance guidelines based on different properties, considering both learning-related metrics, such as accuracy, and system-related metrics, such as run time and memory footprint. We further propose and analyse generalized performance metrics, which facilitate the model evaluation phase in segmentation tasks allowing users to select the most appropriate approach for their context in terms of accuracy and efficiency.

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