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Point Cloud Library: A study of different features part of the Point Cloud Library

Point Cloud Library: A study of different features part of the Point Cloud Library

Figure 20: Test case 3 IX. CONCLUSION AND FUTURE WORK As we can see, the results of the algorithm are remarkable and it successfully identified the object itself and other object clusters similar to it. In fact, the reported object recognition accuracy is as high as 98.52 % for a training set containing 55000 point clouds [4]. This also shows that the Point Cloud Library and its algorithms are scalable to large datasets. In addition to that, they work well with noisy data and the processing time is quite fast at 0.3 ms/cluster, making it ideal to use in real world applications.
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SELF-LEARNING ONTOLOGY FOR INSTANCE SEGMENTATION OF 3D INDOOR POINT CLOUD

SELF-LEARNING ONTOLOGY FOR INSTANCE SEGMENTATION OF 3D INDOOR POINT CLOUD

2. RELATED WORKS The first challenge in pure segmentation frameworks is to obtain group of points that can describe the organization of the data by a relevant clustering with enough detachment. The work of Weber et al. provides the first approach of using relationships while conserving the point-based flexibility (Weber et al., 2010). They propose an over-segmentation algorithm using ‘supervoxels’, an analogue of the superpixel approach for 2D methods. Based on a local k-means clustering, they try and group the voxels with similar feature signatures (39-dimensional vector) to obtain segments. The work is interesting because it is one of the earliest to try and propose a voxel-clustering with the aim of proposing a generalist decomposition of point cloud data in segments. Son et Kim use such a structure in (Son and Kim, 2017) for indoor point cloud data segmentation. They aim at generating the as-built BIMs from laser-scan data obtained during the construction phase. Their approach consists of three steps: region-of-interest detection to distinguish the 3D points that are part of the structural elements to be modelled, scene segmentation to partition the 3D points into meaningful parts comprising different types of elements while using local concave and convex properties between structural elements, and volumetric representation. The approach clearly shows the dominance of planar features in man-made environments. Another very pertinent work is (Wang et al., 2017), which proposes a SigVox descriptor. The paper first categorizes object recognition task following the approach of: (1) model-fitting based (starts with segmenting and clustering point cloud, followed by fitting point segments); (2) semantic methods (based on a set of rule-based prior knowledge); and, (3) shape-based methods (shape featuring from
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Improving thin structures in surface reconstruction from sparse point cloud

Improving thin structures in surface reconstruction from sparse point cloud

Methods use lines or curves and organize them as graphs, which are used as scaffolds for surface reconstruction. In [22], curves are reconstructed from image gradient and the surface is obtained by curve interpolation (lofting) and occlusion reasoning. In [24], an initial surface mesh is regularized by back-projecting linear structures that are semi-automatically selected from the images. Other methods estimate dense depth maps including pixels at occluding contours, then merge them in a voxel grid. In [18], quasi-dense depth maps of internet images are densified by encouraging depth discontinuities at image contours. In [27], the depths of video sequences are first computed in high-gradient regions including silhouettes, then they are propagated to low-gradient regions. Using a point cloud reconstructed from images of a scene with planar structures, [5] segments into inside and outside a tetrahedralization of the points by using graph-cut and two regularizations: horizontal slicing and a smoothness term based on image lines.
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A Built Heritage Information System Based on Point Cloud Data: HIS-PC

A Built Heritage Information System Based on Point Cloud Data: HIS-PC

2.3. Heritage BIM and its Limitation Since the last couple of years, the research community has focused on the ability to interpret surveys as parametric shapes or features to fit the BIM standard modelling approach. Most of the proposed works focused on point cloud data as they now represent the mainstream of captured information on the heritage site, whatever information is collected via TLS or photogrammetric processes. The so-called Scan-to-BIM aims at improving the feature recognition and reduce the treatment time to interpret, structure and create BIM objects from a point cloud [29–32]. It has to be said that for most of the cultural heritage application, those approaches are not feasible due to the time that is required to generate a complete, accurate and controlled model of heritage sites. Moreover, the interpretation of heritage objects is performed in a certain context that depends on the experience of the operator performing the recognition (or who control the automated feature recognition). As mentioned before, the management of a cultural heritage site requires the collaboration of many specialists. Each of the specialties has to interpret the reality regarding the requirement of their field. It means, for example, that a built archaeologist and an architect will not segment a building in the same way. Therefore, the information structuration is greatly complexified to manage all viewpoints [33]. Some projects tried to face this problem [34–38], the most efficient application rely on data structure managing temporal information [39] or not that are not directly related to BIM information structure but on ontologies [40], relational databases [41,42] or NoSQL data structures [43].
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Automatic Clustering Of Celtic Coins Based On 3d Point Cloud Pattern Analysis

Automatic Clustering Of Celtic Coins Based On 3d Point Cloud Pattern Analysis

1. INTRODUCTION The Celts are well-known for the high quality of their metal- lurgical productions (shields, weapons, jewels, helmets, coins, and so on). In order to better understand their manufacturing methods, archaeologists rely on detailed observations of ob- jects, in which 3D scans are becoming increasingly important. Celtic coins can give very useful information to better under- stand the socio-economic context. Coins are among the first mass-produced objects with repeatability constraints imposed on composition, shape, size, weight and image. The image is the mark of the issuing power. For archaeologists, detailed analysis of coins are therefore crucial (Richard, Lopez, 2014, Gruel, 1981, Brousseau et al., 2009). One aim is to estimate the number of dies 1 used in the process of fabrication to estimate the volume of money supply. To do so, archaeologists compare the pattern of coins and try to see if they are struck by the same die. As we can see in Figure 1, the problem is difficult because the differences between patterns are hardly perceptible for an untrained eye (the coins are about 2 cm wide). Moreover, the coins may be damaged, worn or even fractured. Archaeologists can find thousands of coins and it would take too much time to compare every pair of coins. That’s why, they need auto- matic tools to help them to know whether the coins come from the same die or not. To solve this challenging task, we pro- pose a rather simple but effective method to know automatically whether two coins come from the same die and to cluster them. To recognize dies, we first align the patterns using a registration algorithm. Then, by analyzing the histograms of point to point distance maps, we can tell whether two coins are struck by the same dies or not. With all these comparisons, it is possible then to cluster coins that have the same die. Registration algorithms are algorithms which align 3D point cloud and are usually used for reconstruction, SLAM (Simultaneous Odometry And Map- ping) or even 3D object tracking (Vongkulbhisal et al., 2017). But aligning partially scanned scenes or 3D models and align- ing patterns are different. Indeed, registration algorithms usu- ally align shapes, whereas in our problem, we want to align patterns in arbitrary shapes. That’s why we need to adapt regis-
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3D Point Cloud Analysis for Automatic Inspection of Aeronautical Mechanical Assemblies

3D Point Cloud Analysis for Automatic Inspection of Aeronautical Mechanical Assemblies

(a) (b) (c) (d) Figure 3: Example of our dataset. (a) CAD model, (b) 2D image, (c) 3D point cloud and (d) 3D point cloud with texture 3. OFFLINE PROCESS: VIEWPOINT SELECTION The initial setup of an inspection task for a 3D free-form surface cannot be done manually, because a human operator cannot define with sufficient accuracy the camera position that will allow him to get the best viewpoint of the element to be inspected. Therefore, we need a (semi-)automatic offline configuration process that is used to compute the best viewpoints which can help to improve the quality and the efficiency of inspection. 8 Therefore, the camera needs to be positioned in
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Automatic extraction and management of semantics within point cloud data

Automatic extraction and management of semantics within point cloud data

3. Poux, F.; Neuville, R.; Van Wersch, L.; Nys, G.-A.; Billen, R. 3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects. Geosciences 2017, 7, 96. 4. Poux, F.; Billen, R. Smart point cloud: Toward an intelligent documentation of our world. In Proceedings of the PCON; Liège, 2015; p. 11. 5. Poux, F.; Neuville, R.; Hallot, P.; Billen, R. Point clouds as an efficient multiscale layered spatial representation. In Proceedings of the

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Summarizing Large Scale 3D Point Cloud for Navigation Tasks

Summarizing Large Scale 3D Point Cloud for Navigation Tasks

I. I NTRODUCTION Last years, the introduction of High-Definition (HD) and semantic maps has made a great participation in the large commercial success of navigation and mapping products and also in the enhancement of data fusion based localization algorithms. Several digital map suppliers like TomTom and HERE are now providing HD maps with higher navigation accuracy, especially in challenging urban environments. On the one hand, these HD maps provide more detailed representation of the environment even within large-scale 3D point cloud data. On the other hand, they require a high processing capacity with severe time constraints as well as a large storage requirement. Hence the need to find a new method to sum- marize these maps in order to reduce the required resources (computation / memory) to run the intelligent transportation systems while preserving the essential navigation information (saliency pixels, important nodes, etc.).
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Navigability Graph Extraction From Large-Scale 3D Point Cloud

Navigability Graph Extraction From Large-Scale 3D Point Cloud

mapping using Octrees to eliminate redundant information. Another compression algorithms have been proposed in [9], [10]. They aim to select 3D points from the initial map based on hundreds of descriptors requiring significant memory size. A novel compression method has been proposed in [7]. This method consists in sampling a point cloud by selecting the features useful for future relocalization. An approach to map reduction was proposed in [22]. It aims to select only the places that are particularly suitable for localization using the location utility metric. To simplify the process of appearance- based navigation, a selection process is applied to choose the key/reference features in the environment. For instance, in visual memory based approaches, a set of relevant and distinctive areas (images) are acquired and used during navigation for comparison with the current position. In the work of Cobzas [6], a panoramic memory of images is cre- ated by combining acquired images with depth information extracted from a laser scanner. In this image database, only the salient information will be retained [4] without degrading the performance during navigation. In order to build this image database, some techniques have been developed to guarantee the maximum efficiency in the choice of useful information. A spherical representation has been proposed by M. Meilland et al. [15]. This spherical representation is built by merging different images acquired by a set of cameras with the depth information extracted from a laser scanner. All existing methods for summarizing maps are based mainly on geometric or photometric characteristics to select the most salient information. However, these characteristics are insufficient for good perception and understanding of the environment. A combination of geometric, photometric and semantic characteristics when selecting information allows us to have a compact, precise and useful summary. Our work aims to perform several navigation tasks using only a map summary of the environment. This map should be not only compact but also coherent with the perception of the agent. To provide this map summary, we propose a new method dealing with large-scale 3D point clouds. The output of our summarizing method is a set of spherical images. Our main contributions are:
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Improving LiDAR Point Cloud Classification using Intensities and Multiple Echoes

Improving LiDAR Point Cloud Classification using Intensities and Multiple Echoes

Improving LiDAR Point Cloud Classification using Intensities and Multiple Echoes Christophe Reymann 1,2 and Simon Lacroix 1,3 Abstract— Besides precise and dense geometric information, some LiDARs also provide intensity information and multiple echoes, information that can advantageously be exploited to enhance the performance of the purely geometric classification approaches. This information indeed depends on the physical nature of the perceived surfaces, and is not strongly impacted by the scene illumination – contrary to visual information. This article investigates how such information can augment the precision of a point cloud classifier. It presents an empirical evaluation of a low cost LiDAR, introduces features related to the intensity and multiple echoes and their use in a hierarchical classification scheme. Results on varied outdoor scenes are depicted, and show that more precise class identification can be achieved using the intensity and multiple echoes than when using only geometric features.
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From point cloud to BIM: a survey of existing approaches

From point cloud to BIM: a survey of existing approaches

In order to handle more efficiently projects of restoration, documentation and maintenance of historical buildings, it is essential to rely on a 3D enriched model for the building. Today, the concept of Building Information Modelling (BIM) is widely adopted for the semantization of digital mockups and few research focused on the value of this concept in the field of cultural heritage. In addition historical buildings are already built, so it is necessary to develop a performing approach, based on a first step of building survey, to develop a semantically enriched digital model. For these reasons, this paper focuses on this chain starting with a point cloud and leading to the well-structured final BIM; and proposes an analysis and a survey of existing approaches on the topics of: acquisition, segmentation and BIM creation. It also, presents a critical analysis on the application of this chain in the field of cultural heritage
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Lexicographic optimal homologous chains and applications to point cloud triangulations

Lexicographic optimal homologous chains and applications to point cloud triangulations

the cardinal of the vertex set [32]. Assuming sorted edges as input of the algorithm – which is performed in O(n ln(n)), the algorithm runs in O(nα(n)) time complexity. 6 Application to point cloud triangulation In all that precedes, the order on simplices was not specified and one can wonder if choosing such an ordering makes the specialization of OCHP too restrictive for it to be useful. In this section, we give a concrete example where this restriction makes sense and provides a simple and elegant application to the problem of point cloud triangulation. Whereas all that preceded dealt with an abstract simplicial complex, we now consider a bijection between vertices and a set of points in Euclidean space, allowing to compute geometric quantities on simplices.
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Point Cloud vs. Mesh Features for Building Interior Classification

Point Cloud vs. Mesh Features for Building Interior Classification

Prior to the tests, each dataset is segmented into smooth planar segments as shown in Table 5 and Figures 4 – 6 . Both datasets were segmented with similar parameters to achieve a proper segmentation of the building interiors. Table 5 depicts the number of segments that was created for each class. Overall, proper segments are extracted from both datasets. However, there are several differences between both procedures. First, the processing times of the point clouds are significantly higher than those of the meshes. This is expected since the meshing (of which the processing time is not included here) reduces the number of observations by over 90% compared to the initial point cloud. Second, the number of segments created from the point clouds are significantly higher than with the meshes due to the increased noise, observations and holes. Floors are an exception to this as these segments contain little curvature and detailing and are less obscured by other objects in comparison to other object types. These differences impact the processing time of the segmentation and the classification of the point clouds. However, this is counter-balanced by the fact that the point cloud does not necessitate any preprocessing step to transform it into a mesh, which is often a very time-consuming process. The resulting point-based and mesh-based segments S are the main input for the feature extraction, the classification and the visual inspection.
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Point-cloud avatars to improve spatial communication in immersive collaborative virtual environments

Point-cloud avatars to improve spatial communication in immersive collaborative virtual environments

experimenter had to select the cubes by physically moving his body and his arm towards them. The participants used then the real-time body and arm movements as a feedback from the confederate to their verbal guidance. When the kinematic fidelity of the arm movements was low (preconstructed avatar condition), the participants may have had some difficulties to determine whether the experimenter was about to grasp the correct object. Therefore, they had to direct their attention towards the hand and arm movements instead of the whole body movement. This is supported by the increased number of participants (45%) reporting feeling some strangeness looking at the avatar’s arms suggesting that the participants found the arm movements in this condition less precise. On the other hand, the higher arms kinematic fidelity in the point-cloud avatar condition encouraged the participants (90% of them) to use the whole avatar body as a frame of reference with a higher success rate in this case. Moreover, none of them have reported any strangeness looking at the avatar’s arms. H5 is then validated for task 1. This further supports the fact that a higher kinematic fidelity is more useful during the guidance task.
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Unsupervised segmentation of indoor 3D point cloud: application to object-based classification

Unsupervised segmentation of indoor 3D point cloud: application to object-based classification

Figure 10 Segmentation results on a noisy indoor point cloud captured by a hand-held laser scanner Zeb-Revo In particular, we found that we would often overestimate the ideal values for ε and τ to avoid a costly re-computation of normal estimate and segmentation. In contrast, lower values for these parameters would afford a better extraction of detailed information from the point cloud. Generally, results show that the approach outperforms conventional methods both in computing performances and accuracy. It is very robust to noise, misadjusted density and provides a clear hierarchical point grouping where fully unsupervised parameter estimation gives better results than "user-defined" parameters. The presented method is easy to implement. It is independent from any high- end GPUs, and mainly leverages the processor and the Random- Access Memory in its current state. It is crucial for many companies that do not possess high-end servers, but rather web- oriented (no GPU, low RAM, and Intel Core processors). As such, it is easily deployable on a client-server infrastructure, without the need to upgrade the server-side for offline computations. It is to expect a coherent result within 10 minutes for a dataset of 100 million points.
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Rapid clustering from colorized 3D point cloud data for reconstructing building interiors

Rapid clustering from colorized 3D point cloud data for reconstructing building interiors

roberto.canas@nrc.gc.ca Abstract - Range scanning of building interiors generates very large, partially spurious and unstructured point cloud data. Accurate information extraction from such data sets is a complex task due to the presence of multiple objects, diversity of their shapes, large disparity in the feature sizes, and the spatial uncertainty due to occluded regions. A fast segmentation of such data is necessary for quick understanding of the scanned scene. Unfortunately, traditional range segmentation methodologies are computationally expensive because they rely almost exclusively on shape parameters (normal, curvature) and are highly sensitive to small geometric distortions in the captured data. This paper introduces a quick and effective segmentation technique for large volumes of colorized range scans from unknown building interiors and labelling clusters of points that represent distinct surfaces and objects in the scene. Rather than computing geometric parameters, the proposed technique uses a robust Hue, Saturation and Value (HSV) color model as an effective means of identifying rough clusters (objects) that are further refined by eliminating spurious and outlier points through region growth and a fixed distance neighbors (FDNs) analysis. The results demonstrate that the proposed method is effective in identifying continuous clusters and can extract meaningful object clusters, even from geometrically similar regions.
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3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

Geometric feature descriptors: As such, predominant algorithms for geometric featuring in scientific literature are RANSAC [ 58 – 66 ], Sweeping [ 67 ], Hough [ 68 – 71 ] and PCA [ 61 , 72 – 77 ]. The authors [ 61 , 78 ] provide a robust PCA approach for plane fitting. The paper by Sanchez [ 63 ] primarily makes use of RANSAC to detect most building interiors, that may be modelled as a collection of planes representing ceilings, floors, walls and staircases. Mura et al. [ 79 ] partitions an input 3D model into an appropriate number of separate rooms by detecting wall candidates and then studying the possible layout by projecting the scenarios in a 2D space. Arbeiter et al. [ 80 ] present promising descriptors, namely the Radius-Based Surface Descriptor (RSD), Principal Curvatures (PC) and Fast Point Feature Histograms (FPFH). They demonstrate how they can be used to classify primitive local surfaces such as cylinders, edges or corners in point clouds. More recently, Xu et al. [ 81 ] provide a 3D reconstruction method for scaffolds from a photogrammetric point cloud of construction sites using mostly point repartitions in specific reference frames. Funkhouser et al. [ 82 ] also propose a matching approach based on shape distributions for 3 models. They pre-process through random sampling to produce a continuous probability distribution later used as a signature for each 3D shape. The key contribution of this approach is that it provides a framework within which arbitrary and possibly degenerate 3D models can be transformed into functions with natural parameterizations. This allows simple function comparison methods to produce robust dissimilarity metrics and will be further investigated in this paper.
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Semantic enrichment of point cloud by automatic extraction and enhancement of 360° panoramas

Semantic enrichment of point cloud by automatic extraction and enhancement of 360° panoramas

The raw nature of point clouds is an important challenge for their direct exploitation in architecture, engineering and construction applications. Particularly, their lack of semantics hinders their utility for automatic workflows (Poux, 2019). In addition, the volume and the irregularity of the structure of point clouds makes it difficult to directly and automatically classify datasets efficiently, especially when compared to the state-of-the art 2D raster classification. Recently, with the advances in deep learning models such as convolutional neural networks (CNNs) , the performance of image-based classification of remote sensing scenes has improved considerably (Chen et al., 2018; Cheng et al., 2017). In this research, we examine a simple and innovative approach that represent large 3D point clouds through multiple 2D projections to leverage learning approaches based on 2D images. In other words, the approach in this study proposes an automatic process for extracting 360° panoramas, enhancing these to be able to leverage raster data to obtain domain-base semantic enrichment possibilities. Indeed, it is very important to obtain a rigorous characterization for use in the classification of a point cloud. Especially because there is a very large variety of 3D point cloud domain applications. In order to test the adequacy of the method and its potential for generalization, several tests were performed on different datasets. The developed semantic augmentation algorithm uses only the attributes X, Y, Z and camera positions as inputs.
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3D Point Cloud Analysis for Detection and Characterization of Defects on Airplane Exterior Surface

3D Point Cloud Analysis for Detection and Characterization of Defects on Airplane Exterior Surface

Abstract Three-dimensional surface defect inspection remains a challenging task. This paper describes a novel automatic vision-based inspection system that is capa- ble of detecting and characterizing defects on an air- plane exterior surface. By analyzing 3D data collected with a 3D scanner, our method aims to identify and ex- tract the information about the undesired defects such as dents, protrusions or scratches based on local sur- face properties. Surface dents and protrusions are iden- tified as the deviations from an ideal, smooth surface. Given an unorganized point cloud, we first smooth noisy data by using Moving Least Squares algorithm. The curvature and normal information are then estimated at every point in the input data. As a next step, Re- gion Growing segmentation algorithm divides the point cloud into defective and non-defective regions using the local normal and curvature information. Further, the convex hull around each defective region is calculated in order to englobe the suspicious irregularity. Finally, we use our new technique to measure the dimension, depth, and orientation of the defects. We tested and validated our novel approach on real aircraft data ob- tained from an Airbus A320, for di↵erent types of de- fect. The accuracy of the system is evaluated by com- paring the measurements of our approach with ground truth measurements obtained by a high-accuracy mea- suring device. The result shows that our work is robust, e↵ective and promising for industrial applications.
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3D Plant Phenotyping: All You Need is Labelled Point Cloud Data

3D Plant Phenotyping: All You Need is Labelled Point Cloud Data

Some of the difficulties associated with creating labeled 3D point cloud datasets include the expensive scanning devices, cumbersome and error prone manual annotation of large number of points, and reconstructing full 3D struc- ture of complex plants in the presence of occlusion and noise. Also, growing varieties of plant species and acquiring data at different growth stages require lot of manpower and constant monitoring of the plants. In this regard, syn- thetic (or virtual) plant models can play an important role. Recently Ward et al. [ 42 ] demonstrated that synthetic data can be extremely useful in training deep learning models, which can be reliably used for measurements of different types of phenotypic traits in the general case. Modelling the geometry of com- plex plant structures have been a center of attention for mathematicians and biologists for decades [ 23 , 15 , 14 , 29 ]. Virtual models can be extremely useful in agricultural studies [ 12 , 3 ], as well as have great potential for mechanical simula- tion of plant behavior [ 4 ]. In generating the virtual plant models, L-system based modelling technique has been quite successful [ 28 ]. Different types of platforms have been developed to simulate the L-system rules, e.g., L+C modelling lan- guage [ 18 ], L-Py framework [ 2 ], etc. Functional Structural Plant Models (FSPM) [ 15 ] have emerged as powerful tool to construct 3D models of plant functioning and growth. These models can play an important role in mechanical simulation in crop science research [ 39 ]. Although generation of synthetic plant models have been a well studied area, the virtual plant models have not been exploited in generating synthetic 3D point cloud data for plant phenotyping applications. Compared to the synthetic artery data simulation software like Vascusynth [ 16 ] for medical imaging research, there is no such tool available in the plant pheno- typing community.
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