DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot.

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

The segmentation of crop organs from 3D laser point clouds is an important prerequisite work of crop phenotypic parameters in non-destructive measurement. This paper respectively selected the 3D point cloud data of the rapeseed plant with leaf stage and pod stage as the research materials. A novel normal vector-based method for segmentation of the 3D point cloud is presented. First, a 3D scanner, HandyScan 300, was used to obtain 3D point cloud data. Second, using the voxel-based grid method, the original point cloud data were down-sampled at the premise of keeping the shape of point cloud unchanged. Third, according to the characteristics of the point cloud, the two conditions of the normal vector difference and the Euclidean distance between each point could be merged into two necessary conditions of the current class. Finally, the nearest point was searched with a set of labeled point cloud growth and through each point cloud of European radius until the collection of point cloud and the adjacent candidate was in accordance with the current conditions of the finished classification process. Results showed that the angle difference threshold of the normal vector was [0.91, 0.95]. The segmentation effect of the point cloud data of the leaves of the rapeseed plant was the best, which avoided the problem of misclassification and the appearance of over-segmentation. The angle difference threshold of the normal vector was [0.88, 0.91]. The segmentation effect of the point cloud data of the pod of the rapeseed plant was the best, and the accuracy rate reached 97%. Therefore, the validity and feasibility of the method was verified. Accurate segmentation of the plant organ is another foundation for the nondestructive measurement of the phenotypic parameters in the later stage.

Plant phenotypic analysis is of great importance to the development of agricultural engineering, and is one of the core issues in crop science and plant breeding. Since plant growth is spatio-temporal and synchronous, understanding the growth and development of individual plants can help to reveal the growth potential of the whole plot and thus improve planting methods. In recent years, the technical means to analyze the growth situation using 3D point cloud data has received extensive attention. The plant point cloud obtained by scanning plants with LiDAR has the characteristics of high resolution, high precision, etc. Periodic scanning of the same plant for spatio-temporal point cloud data sets allows monitoring of growth through subtle changes of plant organs. Organ tracking of growing plants remains challenging due to the lightward nature of growth, the potential for topological changes and the unpredictability of plant growth over time, with the possibility of new leaf growth and leaf death. This paper designs a plant organ growth tracking method based on point cloud. First of all, for growing plants, we have established a crop point cloud spatio-temporal dataset based on two publicly available point cloud datasets. The data set includes four species, tomato, tobacco, sorghum and maize, each species contains complete organ instance labels, and each organ of the same plant has a unique label, which means that the labels of the same organ of an individual at different scan dates correspond one-to-one. Second, this paper proposes a point cloud data-based plant organ growth tracking method, which uses a cost correlation matrix to automatically track growing plant organs. Finally, based on the set of quantitative evaluation metrics, our algorithm achieves a matching accuracy of 82.89% on the plant spatio-temporal dataset and good growth tracking results in the qualitative analysis.

Aiming at the shortcomings of the existing 3D point cloud data automatic extraction methods of substation equipment, which are highly dependent on big data algorithms and low efficiency, this paper proposes a 3D LIDAR point cloud data segmentation method and process based on the multidimensional subspace grid density difference. The proposed method is based on eliminating the flying spots of 3D point cloud data, and is divided into equipment point cloud data and ground point cloud data based on point cloud data characteristics for 3D real-world modeling and accurate positioning of the model; Among them, the equipment point cloud data uses a multi-dimensional density difference segmentation method. The long-distance terrain is divided in the XOY and YOZ planes, and converted into a combination of multiple small-scale scale spaces. Effective segmentation, so that automatic extraction of substation equipment can be realized; The ground point cloud data uses a single-dimensional density difference segmentation method to dilute the ground point cloud data to obtain clear positioning points. The feasibility verification results of cloud data of a UHV substation show that the proposed method can effectively suppress the noise interference of interference points, realize accurate extraction and location of substation equipment, and the algorithm has high efficiency and strong engineering application.

To quantify the architecture and select the ideal ideotype, it is vital to accurately measure the dimension of each part of the mantis shrimp. Point clouds have become increasingly popular in recent years as an efficient solution. However, the current manual measurement is labor intensive and costly and has high uncertainty. Automatic organ point cloud segmentation is a prerequisite and core step for phenotypic measurements of mantis shrimps. Nevertheless, little work focuses on mantis shrimp point cloud segmentation. To fill this gap, this paper develops a framework for automated organ segmentation of mantis shrimps from multiview stereo (MVS) point clouds. First, a Transformer-based MVS architecture is applied to generate dense point clouds from a set of calibrated phone images and estimated camera parameters. Next, an improved point cloud segmentation (named ShrimpSeg) that exploits both local and global features based on contextual information is proposed for organ segmentation of mantis shrimps. According to the evaluation results, the per-class intersection over union of organ-level segmentation is 82.4%. Comprehensive experiments demonstrate the effectiveness of ShrimpSeg, outperforming other commonly used segmentation methods. This work may be helpful for improving shrimp phenotyping and intelligent aquaculture at the level of production-ready.

Accurate simultaneous semantic and instance segmentation of a plant 3D point cloud is critical for automatic plant phenotyping. Classically, each organ of the plant is detected based on the local geometry of the point cloud, but the consistency of the global structure of the plant is rarely assessed. We propose a two-level, graph-based approach for the automatic, fast and accurate segmentation of a plant into each of its organs with structural guarantees. We compute local geometric and spectral features on a neighbourhood graph of the points to distinguish between linear organs (main stem, branches, petioles) and two-dimensional ones (leaf blades) and even 3-dimensional ones (apices). Then a quotient graph connecting each detected macroscopic organ to its neighbors is used both to refine the labelling of the organs and to check the overall consistency of the segmentation. A refinement loop allows to correct segmentation defects. The method is assessed on both synthetic and real 3D point-cloud data sets of Chenopodium album (wild spinach) and Solanum lycopersicum (tomato plant).

In recent years, with the rapid development of 3D acquisition technology, point clouds are playing an increasingly important role in fields such as computer vision, autonomous driving and robotics. In the semantic segmentation task of 3D point clouds, most of the current point cloud segmentation networks tend to ignore the relationship between points when learning the local features of point clouds, which leads to inadequate extraction of local geometric features of point clouds. To solve this problem, this paper proposes a 3D point cloud segmentation network model DPGNN based on dynamic graph convolution. This model optimizes point cloud local features based on PointNet++ processing of point clouds. A dynamic graph convolution module is designed to replace the local feature extraction in PointNet++. The module can dynamically generate the local area graph structure of points and use a multilayer perceptron to extract the features of edges in the graph structure. In this paper, scene segmentation and part segmentation experiments are conducted on S3DIS and ShapeNet datasets, respectively. The overall accuracy in indoor scene segmentation reaches 88.27%, which is 6.01% better than the benchmark network PointNet++; the average class intersection ratio in part segmentation reaches 85.3%, which is 0.2% better than the benchmark network. The results show that the dynamic graph convolution module designed in this paper can effectively improve the accuracy of the model on point cloud segmentation, and the DPGNN network outperforms most of the current mainstream point cloud segmentation networks.

Facade structures from three‐dimensional (3D) point cloud data (PCD) and two‐dimensional (2D) optical images can provide significant information for 3D building modeling. However, a unified data model for integrating 2D imagery pixels and 3D PCD is absent in current methods, leading to a complex implementation process, large calculations, and inefficiency. An efficient facade structure extraction method for building facades is proposed in this study. Based on the conversion matrix, 2D image and 3D PCD information are merged to build an image‐based laser point cloud (ILPC) data model first. Second, both the line segment detection and random sample consensus algorithms are improved according to the structure and characteristics of the ILPC data model. Finally, building facade structures are extracted and optimized. Facade structures can be extracted accurately and efficiently by the proposed method, which contains rich information support from the ILPC data model. The proposed method extracts fine building facade structures with accuracy over 0.68 in all experiments and recall up to 0.81, which are better than the Wang method. Extracted structures constitute valuable support for numerous fields, such as 3D building modeling and building information modeling construction.

Abstract. Foundation models in computer vision, such as the Segment Anything Model (SAM), have demonstrated remarkable zero-shot performance in image segmentation. Leveraging these models for automated building segmentation can contribute to the efficiency of Scan-to-BIM workflows. Automatic 3D modelling has become widely relied on point cloud data; however, the nature of this data hinders the direct application of the foundation models. This study explores the potential use of SAM for automatic point cloud segmentation, proposing a SAM-based approach for segmenting building components, such as rooms, doors, and windows. The proposed method employs SAM to generate masks for an image that represents projected point clouds. Point clouds are then retrieved for each mask, which are further classified to identify building components. Room segmentation starts with the extraction of a section that defines the room boundary, followed by horizontal projection of the section. In contrast, door and window segmentation starts by projecting planes containing wall points onto their normal vectors. The experiments have been performed using three real case studies. The findings demonstrate the method's effectiveness without requiring any pretraining process, highlighting that the application of the foundation models in point cloud segmentation is a promising direction.

A joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds

A general and flexible point cloud simplification method based on feature fusion

The escalating incorporation of three-dimensional (3D) point cloud data across industrial applications highlights the necessity of assuring its reliability. The error-prone process of object digitization, the large data volumes, and equipment inaccuracies can lead to the degradation of 3D point cloud data quality by introducing varying degrees of noise, outliers, and missing values. Therefore, there is a pressing need for a generally applicable, comprehensive, and robust solution to effectively validate the integrity of point cloud data and assess its quality. Such a solution would empower professionals to rely on this data for critical decision-making, covering applications within many domains, such as manufacturing, automotive, and robotics. In this article, we propose, apply, and assess the 3D Point Cloud Data Validation (3D-DaVa) pipeline, an automated 3D data validation system incorporating statistical and machine learning techniques. The pipeline takes a point cloud and its reference as input and outputs accuracy, validity, and completeness scores. We demonstrated the efficacy of 3D-DaVa through a rigorous evaluation using both real-world manufacturing data and openly available data, where we deliberately introduced distortions covering five distortion levels by simulating common inaccuracies. The data quality assessment results obtained by varying distortion levels reveal a decreasing trend with a distortion increase, thus underscoring the 3D-DaVa’s capability to quantify such deviations accurately.

Tobacco stem and leaf segmentation and phenotypic parameter extraction based on the improved point cloud segmentation network PE-KPConv

Point cloud registration from laser scanning data is a technique to establish the mapping relationship between source and target point clouds, which has been widely used in automatic 3D reconstruction, pose estimation, localization, and navigation. While algorithms like Super4PCS and MSSF-4PCS can achieve registration without initial poses, they are relatively slow, less accurate, and require iterations. To address these issues, we propose a 3D point cloud registration algorithm based on interval segmentation and multi-dimensional feature. Firstly, the source and target point clouds are segmented internally and the point cloud curvature is designed to narrow down the search range for the registration between the segmented point clouds. Secondly, the corresponding four-point sets in the segmented areas of the source and target point clouds are determined using affine invariance constraints. Finally, a multi-dimensional feature vector based on curvature features and fast point feature histogram is established to determine the unique corresponding four-point set pairs, and the rigid body transformation matrix is solved accordingly. Our algorithm is tested on publicly available 3D point cloud data models Bunny, Dino, Dragon, and Horse from Stanford University. Results showed that our algorithm improved registration accuracy by 24.39% and registration efficiency by 46.21% compared to the MSSF-4PCS point cloud registration algorithm. Multiple sets of experimental results confirmed the effectiveness of our algorithm. The proposed 3D point cloud registration is proved to be fast with high accuracy, which can be utilized for automatic segmentation, reconstruction, and modelling from Laser Scanning Data.

3D digital documentation for buildings has become a necessary tool in preserving them. Heritage buildings are exposed from various kind of threats such as human negligence, natural disaster and weather changes. The fundamental in 3D digital documentation which is the 3D point cloud data has captures great attention and has widely used in many fields due to the availability of laser scanners. The use of laser scanning in engineering surveys is gaining attention due to its advantage of producing high accuracy data. In most situations, it also able to scan the entire required site, thus offers a good potential technique for large-scale applications like for heritage buildings preservation. The data, which consists of high density of points, can be delivered in a short time. However, this causes a massive amount of data generated and hence, it becomes very difficult to be managed. Due to this issue, there are critical needs to have a good method in managing 3D point cloud data to maintain features and visualization of buildings, specially the old and aged ones. This paper will review developed methods in handling these data, concentrating on two specific processes, which are data structure and data filtering. The 3D point cloud data is having a unique representation, thus researchers are no longer concentrating on the usual concepts of data registration, meshing and reconstruction to handle it, but data structure and data filtering are preferred. In data structure, mathematical methods incorporating geometric and topological techniques can be used for studying finite set of points. As most of the data captured contains noises and outliers, filtering is also important and can be treated as one of the processes that can be adapted in handling 3D point cloud data. The implementation of various solutions within these areas are presented in this paper and will be analyzed by emphasizing their contributions. Then, results will be studied to explain the effectiveness of the methods used in handling big point data. Finally, some future work for 3D point cloud handling will be highlighted to conclude this critical review focusing in building data for its preservation.