scholarly journals Automatic Detection and Modeling of Underground Pipes Using a Portable 3D LiDAR System

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5345
Author(s):  
Ahmad K. Aijazi ◽  
Laurent Malaterre ◽  
Laurent Trassoudaine ◽  
Thierry Chateau ◽  
Paul Checchin
Keyword(s):  
Point Cloud ◽  
Real Data ◽  
Consensus Algorithm ◽  
3D Point Cloud ◽  
Automated Mapping ◽  
Segmentation Accuracy ◽  
Underground Infrastructure ◽  
Fuzzy C Means Clustering ◽  
3D Scans ◽  
Planning And Construction

Automatic and accurate mapping and modeling of underground infrastructure has become indispensable for several important tasks ranging from urban planning and construction to safety and hazard mitigation. However, this offers several technical and operational challenges. The aim of this work is to develop a portable automated mapping solution for the 3D mapping and modeling of underground pipe networks during renovation and installation work when the infrastructure is being laid down in open trenches. The system is used to scan the trench and then the 3D scans obtained from the system are registered together to form a 3D point cloud of the trench containing the pipe network using a modified global ICP (iterative closest point) method. In the 3D point cloud, pipe-like structures are segmented using fuzzy C-means clustering and then modeled using a nested MSAC (M-estimator SAmpling Consensus) algorithm. The proposed method is evaluated on real data pertaining to three different sites, containing several different types of pipes. We report an overall registration error of less than 7 % , an overall segmentation accuracy of 85 % and an overall modeling error of less than 5 % . The evaluated results not only demonstrate the efficacy but also the suitability of the proposed solution.

scholarly journals AUTOMATIC CLUSTERING OF CELTIC COINS BASED ON 3D POINT CLOUD PATTERN ANALYSIS

2020 ◽  
Vol V-2-2020 ◽  
pp. 973-980
Author(s):  
S. Horache ◽  
F. Goulette ◽  
J.-E. Deschaud ◽  
T. Lejars ◽  
K. Gruel
Keyword(s):  
Point Cloud ◽  
Pattern Analysis ◽  
Experimental Results ◽  
3D Point Cloud ◽  
Automatic Clustering ◽  
Clustering Quality ◽  
3D Scans ◽  
Cloud Pattern ◽  
Graph Based Clustering

Abstract. The recognition and clustering of coins which have been struck by the same die is of interest for archeological studies. Nowadays, this work can only be performed by experts and is very tedious. In this paper, we propose a method to automatically cluster dies, based on 3D scans of coins. It is based on three steps: registration, comparison and graph-based clustering. Experimental results on 90 coins coming from a Celtic treasury from the II-Ith century BC show a clustering quality equivalent to expert’s work.

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 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 DISOCCLUSION OF 3D LIDAR POINT CLOUDS USING RANGE IMAGES

2017 ◽  
Vol IV-1/W1 ◽  
pp. 75-82
Author(s):  
P. Biasutti ◽  
J.-F. Aujol ◽  
M. Brédif ◽  
A. Bugeau
Keyword(s):  
Point Cloud ◽  
Automatic Segmentation ◽  
Image Inpainting ◽  
Real Data ◽  
Point Clouds ◽  
Range Image ◽  
3D Point Cloud ◽  
3D Space ◽  
Variational Image ◽  
3D Lidar

This paper proposes a novel framework for the disocclusion of mobile objects in 3D LiDAR scenes aquired via street-based Mobile Mapping Systems (MMS). Most of the existing lines of research tackle this problem directly in the 3D space. This work promotes an alternative approach by using a 2D range image representation of the 3D point cloud, taking advantage of the fact that the problem of disocclusion has been intensively studied in the 2D image processing community over the past decade. First, the point cloud is turned into a 2D range image by exploiting the sensor’s topology. Using the range image, a semi-automatic segmentation procedure based on depth histograms is performed in order to select the occluding object to be removed. A variational image inpainting technique is then used to reconstruct the area occluded by that object. Finally, the range image is unprojected as a 3D point cloud. Experiments on real data prove the effectiveness of this procedure both in terms of accuracy and speed.

scholarly journals Simulation of simultaneous localization and mapping using 3D point cloud data

2019 ◽  
Vol 16 (2) ◽  
pp. 941
Author(s):  
Shuzlina Abdul-Rahman ◽  
Mohamad Soffi Abd Razak ◽  
Aliya Hasanah Binti Mohd Mushin ◽  
Raseeda Hamzah ◽  
Nordin Abu Bakar ◽  
...  
Keyword(s):  
Point Cloud ◽  
Simultaneous Localization And Mapping ◽  
Real Data ◽  
Light Detection And Ranging ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Localization And Mapping ◽  
Graph Slam ◽  
Robotic Application

<span>Abstract—This paper presents a simulation study of Simultaneous Localization and Mapping (SLAM) using 3D point cloud data from Light Detection and Ranging (LiDAR) technology.  Methods like simulation is useful to simplify the process of learning algorithms particularly when collecting and annotating large volumes of real data is impractical and expensive. In this study, a map of a given environment was constructed in Robotic Operating System platform with Gazebo Simulator. The paper begins by presenting the most currently popular algorithm that are widely used in SLAM namely Extended Kalman Filter, Graph SLAM and Fast SLAM. The study performed the simulations by using standard SLAM with Turtlebot and Husky robots. Husky robot was further compared with ACML algorithm. The results showed that Hector SLAM could reach the goal faster than ACML algorithm in a pre-defined map. Further studies in this field with other SLAM algorithms would certainly beneficial to many parties due to the demands of robotic application.</span>

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

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.

Sign in / Sign up

Export Citation Format

Share Document