scholarly journals PCCT: A POINT CLOUD CLASSIFICATION TOOL TO CREATE 3D TRAINING DATA TO ADJUST AND DEVELOP 3D CONVNET

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 35-40
Author(s):  
E. Barnefske ◽  
H. Sternberg
Keyword(s):  
Network Architecture ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Classification Performance ◽  
Training Data ◽  
Process Step ◽  
Camera Systems ◽  
Classification Tool ◽  
Point Cloud Classification

<p><strong>Abstract.</strong> Point clouds give a very detailed and sometimes very accurate representation of the geometry of captured objects. In surveying, point clouds captured with laser scanners or camera systems are an intermediate result that must be processed further. Often the point cloud has to be divided into regions of similar types (object classes) for the next process steps. These classifications are very time-consuming and cost-intensive compared to acquisition. In order to automate this process step, conventional neural networks (ConvNet), which take over the classification task, are investigated in detail. In addition to the network architecture, the classification performance of a ConvNet depends on the training data with which the task is learned. This paper presents and evaluates the point clould classification tool (PCCT) developed at HCU Hamburg. With the PCCT, large point cloud collections can be semi-automatically classified. Furthermore, the influence of erroneous points in three-dimensional point clouds is investigated. The network architecture PointNet is used for this investigation.</p>

scholarly journals Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 884
Author(s):  
Chia-Ming Tsai ◽  
Yi-Horng Lai ◽  
Yung-Da Sun ◽  
Yu-Jen Chung ◽  
Jau-Woei Perng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Training Data ◽  
Network Architectures ◽  
Point Cloud Data ◽  
Data Types ◽  
Raw Data ◽  
Cloud Data

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

scholarly journals ASSESSING LIDAR TRAINING DATA QUANTITIES FOR CLASSIFICATION MODELS

2021 ◽  
Vol XLVI-4/W4-2021 ◽  
pp. 101-106
Author(s):  
O. Majgaonkar ◽  
K. Panchal ◽  
D. Laefer ◽  
M. Stanley ◽  
Y. Zaki
Keyword(s):  
Point Cloud ◽  
Classification Performance ◽  
Training Data ◽  
Lidar Data ◽  
Data Sets ◽  
Classification Models ◽  
Fundamental Properties ◽  
Point Cloud Classification ◽  
The Impact ◽  
Insight Into

Abstract. Classifying objects within aerial Light Detection and Ranging (LiDAR) data is an essential task to which machine learning (ML) is applied increasingly. ML has been shown to be more effective on LiDAR than imagery for classification, but most efforts have focused on imagery because of the challenges presented by LiDAR data. LiDAR datasets are of higher dimensionality, discontinuous, heterogenous, spatially incomplete, and often scarce. As such, there has been little examination into the fundamental properties of the training data required for acceptable performance of classification models tailored for LiDAR data. The quantity of training data is one such crucial property, because training on different sizes of data provides insight into a model’s performance with differing data sets. This paper assesses the impact of training data size on the accuracy of PointNet, a widely used ML approach for point cloud classification. Subsets of ModelNet ranging from 40 to 9,843 objects were validated on a test set of 400 objects. Accuracy improved logarithmically; decelerating from 45 objects onwards, it slowed significantly at a training size of 2,000 objects, corresponding to 20,000,000 points. This work contributes to the theoretical foundation for development of LiDAR-focused models by establishing a learning curve, suggesting the minimum quantity of manually labelled data necessary for satisfactory classification performance and providing a path for further analysis of the effects of modifying training data characteristics.

scholarly journals AN EFFICIENT DEEP LEARNING APPROACH FOR GROUND POINT FILTERING IN AERIAL LASER SCANNING POINT CLOUDS

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 31-38
Author(s):  
A. Nurunnabi ◽  
F. N. Teferle ◽  
J. Li ◽  
R. C. Lindenbergh ◽  
A. Hunegnaw
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Ground Surface ◽  
Point Clouds ◽  
Urban Environments ◽  
Training Data ◽  
Ground Point ◽  
End To End

Abstract. Ground surface extraction is one of the classic tasks in airborne laser scanning (ALS) point cloud processing that is used for three-dimensional (3D) city modelling, infrastructure health monitoring, and disaster management. Many methods have been developed over the last three decades. Recently, Deep Learning (DL) has become the most dominant technique for 3D point cloud classification. DL methods used for classification can be categorized into end-to-end and non end-to-end approaches. One of the main challenges of using supervised DL approaches is getting a sufficient amount of training data. The main advantage of using a supervised non end-to-end approach is that it requires less training data. This paper introduces a novel local feature-based non end-to-end DL algorithm that generates a binary classifier for ground point filtering. It studies feature relevance, and investigates three models that are different combinations of features. This method is free from the limitations of point clouds’ irregular data structure and varying data density, which is the biggest challenge for using the elegant convolutional neural network. The new algorithm does not require transforming data into regular 3D voxel grids or any rasterization. The performance of the new method has been demonstrated through two ALS datasets covering urban environments. The method successfully labels ground and non-ground points in the presence of steep slopes and height discontinuity in the terrain. Experiments in this paper show that the algorithm achieves around 97% in both F1-score and model accuracy for ground point labelling.

scholarly journals AIRBORNE LIDAR POINT CLOUD CLASSIFICATION BASED ON MULTILEVEL POINT CLUSTER FEATURES

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1231-1237
Author(s):  
Y. Gao ◽  
M. C. Li
Keyword(s):  
Spatial Data ◽  
Point Cloud ◽  
Large Scale ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Location Based Services ◽  
Training Data ◽  
Cloud Classification ◽  
Point Set ◽  
Point Cloud Classification

Abstract. Airborne Light Detection And Ranging (LiDAR) has become an important means for efficient and high-precision acquisition of 3D spatial data of large scenes. It has important application value in digital cities and location-based services. The classification and identification of point cloud is the basis of its application, and it is also a hot and difficult problem in the field of geographic information science.The difficulty of LiDAR point cloud classification in large-scale urban scenes is: On the one hand, the urban scene LiDAR point cloud contains rich and complex features, many types of features, different shapes, complex structures, and mutual occlusion, resulting in large data loss; On the other hand, the LiDAR scanner is far away from the urban features, and is like a car, a pedestrian, etc., which is in motion during the scanning process, which causes a certain degree of data noise of the point cloud and uneven density of the point cloud.Aiming at the characteristics of LiDAR point cloud in urban scene.The main work of this paper implements a method based on the saliency dictionary and Latent Dirichlet Allocation (LDA) model for LiDAR point cloud classification. The method uses the tag information of the training data and the tag source of each dictionary item to construct a significant dictionary learning model in sparse coding to expresses the feature of the point set more accurately.And it also uses the multi-path AdaBoost classifier to perform the features of the multi-level point set. The classification of point clouds is realized based on the supervised method. The experimental results show that the feature set extracted by the method combined with the multi-path classifier can significantly improve the cloud classification accuracy of complex city market attractions.

scholarly journals DEEP LEARNING FOR SEMANTIC SEGMENTATION OF 3D POINT CLOUD

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 735-742 ◽  
Author(s):  
E. S. Malinverni ◽  
R. Pierdicca ◽  
M. Paolanti ◽  
M. Martini ◽  
C. Morbidoni ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cultural Heritage ◽  
Point Cloud ◽  
Cultural Landscapes ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Training Data ◽  
Historical Building ◽  
3D Point Clouds

<p><strong>Abstract.</strong> Cultural Heritage is a testimony of past human activity, and, as such, its objects exhibit great variety in their nature, size and complexity; from small artefacts and museum items to cultural landscapes, from historical building and ancient monuments to city centers and archaeological sites. Cultural Heritage around the globe suffers from wars, natural disasters and human negligence. The importance of digital documentation is well recognized and there is an increasing pressure to document our heritage both nationally and internationally. For this reason, the three-dimensional scanning and modeling of sites and artifacts of cultural heritage have remarkably increased in recent years. The semantic segmentation of point clouds is an essential step of the entire pipeline; in fact, it allows to decompose complex architectures in single elements, which are then enriched with meaningful information within Building Information Modelling software. Notwithstanding, this step is very time consuming and completely entrusted on the manual work of domain experts, far from being automatized. This work describes a method to label and cluster automatically a point cloud based on a supervised Deep Learning approach, using a state-of-the-art Neural Network called PointNet++. Despite other methods are known, we have choose PointNet++ as it reached significant results for classifying and segmenting 3D point clouds. PointNet++ has been tested and improved, by training the network with annotated point clouds coming from a real survey and to evaluate how performance changes according to the input training data. It can result of great interest for the research community dealing with the point cloud semantic segmentation, since it makes public a labelled dataset of CH elements for further tests.</p>

scholarly journals Multispectral LiDAR Point Cloud Classification Using SE-PointNet++

2021 ◽  
Vol 13 (13) ◽  
pp. 2516
Author(s):  
Zhuangwei Jing ◽  
Haiyan Guan ◽  
Peiran Zhao ◽  
Dilong Li ◽  
Yongtao Yu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Cloud Classification ◽  
Feature Saliency ◽  
Intensity Information ◽  
Multi Wavelength ◽  
Data Volume ◽  
Point Cloud Classification

A multispectral light detection and ranging (LiDAR) system, which simultaneously collects spatial geometric data and multi-wavelength intensity information, opens the door to three-dimensional (3-D) point cloud classification and object recognition. Because of the irregular distribution property of point clouds and the massive data volume, point cloud classification directly from multispectral LiDAR data is still challengeable and questionable. In this paper, a point-wise multispectral LiDAR point cloud classification architecture termed as SE-PointNet++ is proposed via integrating a Squeeze-and-Excitation (SE) block with an improved PointNet++ semantic segmentation network. PointNet++ extracts local features from unevenly sampled points and represents local geometrical relationships among the points through multi-scale grouping. The SE block is embedded into PointNet++ to strengthen important channels to increase feature saliency for better point cloud classification. Our SE-PointNet++ architecture has been evaluated on the Titan multispectral LiDAR test datasets and achieved an overall accuracy, a mean Intersection over Union (mIoU), an F1-score, and a Kappa coefficient of 91.16%, 60.15%, 73.14%, and 0.86, respectively. Comparative studies with five established deep learning models confirmed that our proposed SE-PointNet++ achieves promising performance in multispectral LiDAR point cloud classification tasks.

scholarly journals GEOMETRY-BASED POINT CLOUD CLASSIFICATION USING HEIGHT DISTRIBUTIONS

2020 ◽  
Vol V-2-2020 ◽  
pp. 259-266
Author(s):  
F. Politz ◽  
M. Sester ◽  
C. Brenner
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Ground Level ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Training Data ◽  
Mountainous Regions ◽  
Dense Image ◽  
The Mean ◽  
Point Cloud Classification

Abstract. Semantic segmentation is one of the main steps in the processing chain for Airborne Laser Scanning (ALS) point clouds, but it is also one of the most labour intensive steps, as it requires many labelled examples to train a classifier. National mapping agencies (NMAs) have to acquire nationwide ALS data every couple of years for their duties. Having point clouds cover different terrains such as flat or mountainous regions, a classifier often requires a refinement using additional data from those specific terrains. In this study, we present an algorithm, which is able to classify point clouds of similar terrain types without requiring any additional training data and which is still able to achieve overall F1-Scores of over 90% in most setups. Our algorithm uses up to two height distributions within a single cell in a rasterized point cloud. For each distribution, the empirical mean and standard deviation are calculated, which are the input for a Convolutional Neural Network (CNN) classifier. Consequently, our approach only requires the geometry of point clouds, which enables also the usage of the same network structure for point clouds from other sensor systems such as Dense Image Matching. Since the mean ground level varies with the observed area, we also examined five different normalisation methods for our input in order to reduce the ground influence on the point clouds and thus increase its transferability towards other datasets. We test our trained networks on four different tests sets with the classes’ ground, building, water, non-ground and bridge.

scholarly journals AIRBORNE LIDAR POINT CLOUD CLASSIFICATION FUSION WITH DIM POINT CLOUD

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 375-382
Author(s):  
M. Zhou ◽  
Z. Kang ◽  
Z. Wang ◽  
M. Kong
Keyword(s):  
Point Cloud ◽  
Sample Selection ◽  
Point Clouds ◽  
Classification Performance ◽  
Airborne Lidar ◽  
Aerial Images ◽  
Dense Image ◽  
Recent Developments ◽  
Selection Framework ◽  
Point Cloud Classification

Abstract. Airborne Light Detection And Ranging (LiDAR) point clouds and images data fusion have been widely studied. However, with recent developments in photogrammetric technology, images can now provide dense image matching (DIM) point clouds. To make use of such DIM points, a sample selection framework is introduced. That is, first, the geometric features of LiDAR points and DIM points are extracted. Each feature per point is considered a sample. Then we extend the binary TrAdaboost classifier into a multi-class one to train all the samples. The classifier automatically assigns weights to the samples in the DIM points. The useful samples are assigned large weights and consequently impact the classification results largely and vice versa. As a result, the useful samples of the DIM points are kept to improve on the LiDAR points classification performance. Because only the samples are used, the registration between the DIM points and LiDAR points is not needed. Moreover, the DIM points capturing similar classes but not the same scene as the LiDAR points can also be used. By our framework, existing aerial images can be fully utilized. For testing the generation ability, the framework is applied in a super-voxel-based classification approach by replacing the points-based features with the super-voxel-based features. In the experiments, whether DIM points at the same places as those of LiDAR are used or not, the results after fusion show that, the LiDAR points classification performance has improved. Also, the better the quality of DIM points are, the better the classification performance achieves.

scholarly journals Comparison of Depth Camera and Terrestrial Laser Scanner in Monitoring Structural Deflections

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 201
Author(s):  
Michael Bekele Maru ◽  
Donghwan Lee ◽  
Kassahun Demissie Tola ◽  
Seunghee Park
Keyword(s):  
Optical Sensors ◽  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Depth Camera ◽  
Terrestrial Laser Scanner ◽  
Cloud Data ◽  
3D Point Clouds ◽  
3D Information

Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than ±10%. However, the result obtained from the TLS were better than those obtained from the DC.

scholarly journals VISUALIZATION OF THE CONSTRUCTION OF ANCIENT ROMAN BUILDINGS IN OSTIA USING POINT CLOUD DATA

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 345-352
Author(s):  
Y. Hori ◽  
T. Ogawa
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Feature Matching ◽  
Three Dimensional ◽  
Point Clouds ◽  
Final Report ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Coverage Accuracy ◽  
New Research

The implementation of laser scanning in the field of archaeology provides us with an entirely new dimension in research and surveying. It allows us to digitally recreate individual objects, or entire cities, using millions of three-dimensional points grouped together in what is referred to as "point clouds". In addition, the visualization of the point cloud data, which can be used in the final report by archaeologists and architects, should usually be produced as a JPG or TIFF file. Not only the visualization of point cloud data, but also re-examination of older data and new survey of the construction of Roman building applying remote-sensing technology for precise and detailed measurements afford new information that may lead to revising drawings of ancient buildings which had been adduced as evidence without any consideration of a degree of accuracy, and finally can provide new research of ancient buildings. We used laser scanners at fields because of its speed, comprehensive coverage, accuracy and flexibility of data manipulation. Therefore, we “skipped” many of post-processing and focused on the images created from the meta-data simply aligned using a tool which extended automatic feature-matching algorithm and a popular renderer that can provide graphic results.

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