scholarly journals DEM Extraction from ALS Point Clouds in Forest Areas via Graph Convolution Network

2020 ◽  
Vol 12 (1) ◽  
pp. 178 ◽  
Author(s):  
Jinming Zhang ◽  
Xiangyun Hu ◽  
Hengming Dai ◽  
ShenRun Qu
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Spatial Relationship ◽  
Point Clouds ◽  
Current Data ◽  
Data Sampling ◽  
Dynamic Graph ◽  
Convolution Model

It is difficult to extract a digital elevation model (DEM) from an airborne laser scanning (ALS) point cloud in a forest area because of the irregular and uneven distribution of ground and vegetation points. Machine learning, especially deep learning methods, has shown powerful feature extraction in accomplishing point cloud classification. However, most of the existing deep learning frameworks, such as PointNet, dynamic graph convolutional neural network (DGCNN), and SparseConvNet, cannot consider the particularity of ALS point clouds. For large-scene laser point clouds, the current data preprocessing methods are mostly based on random sampling, which is not suitable for DEM extraction tasks. In this study, we propose a novel data sampling algorithm for the data preparation of patch-based training and classification named T-Sampling. T-Sampling uses the set of the lowest points in a certain area as basic points with other points added to supplement it, which can guarantee the integrity of the terrain in the sampling area. In the learning part, we propose a new convolution model based on terrain named Tin-EdgeConv that fully considers the spatial relationship between ground and non-ground points when constructing a directed graph. We design a new network based on Tin-EdgeConv to extract local features and use PointNet architecture to extract global context information. Finally, we combine this information effectively with a designed attention fusion module. These aspects are important in achieving high classification accuracy. We evaluate the proposed method by using large-scale data from forest areas. Results show that our method is more accurate than existing algorithms.

scholarly journals ANALYSIS OF 3D BUILDING MODELS ACCURACY BASED ON THE AIRBORNE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-2 ◽  
pp. 797-804 ◽  
Author(s):  
W. Ostrowski ◽  
M. Pilarska ◽  
J. Charyton ◽  
K. Bakuła
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Statistical Parameters ◽  
Airborne Laser ◽  
Building Models

Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term “3D building models” can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

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 Can 3D Point Clouds Replace GCPs?

2014 ◽  
Vol II-5 ◽  
pp. 347-354 ◽  
Author(s):  
G. Stavropoulou ◽  
G. Tzovla ◽  
A. Georgopoulos
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Field Work ◽  
Surface Model ◽  
Control Points ◽  
3D Point Clouds ◽  
Surface Models

Over the past decade, large-scale photogrammetric products have been extensively used for the geometric documentation of cultural heritage monuments, as they combine metric information with the qualities of an image document. Additionally, the rising technology of terrestrial laser scanning has enabled the easier and faster production of accurate digital surface models (DSM), which have in turn contributed to the documentation of heavily textured monuments. However, due to the required accuracy of control points, the photogrammetric methods are always applied in combination with surveying measurements and hence are dependent on them. Along this line of thought, this paper explores the possibility of limiting the surveying measurements and the field work necessary for the production of large-scale photogrammetric products and proposes an alternative method on the basis of which the necessary control points instead of being measured with surveying procedures are chosen from a dense and accurate point cloud. Using this point cloud also as a surface model, the only field work necessary is the scanning of the object and image acquisition, which need not be subject to strict planning. To evaluate the proposed method an algorithm and the complementary interface were produced that allow the parallel manipulation of 3D point clouds and images and through which single image procedures take place. The paper concludes by presenting the results of a case study in the ancient temple of Hephaestus in Athens and by providing a set of guidelines for implementing effectively the method.

scholarly journals Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage

2020 ◽  
Vol 12 (6) ◽  
pp. 1005 ◽  
Author(s):  
Roberto Pierdicca ◽  
Marina Paolanti ◽  
Francesca Matrone ◽  
Massimo Martini ◽  
Christian Morbidoni ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cultural Heritage ◽  
Point Cloud ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Information Modeling ◽  
Dynamic Graph ◽  
Historical Building ◽  
Architectural Elements ◽  
3D Point Clouds

In the Digital Cultural Heritage (DCH) domain, the semantic segmentation of 3D Point Clouds with Deep Learning (DL) techniques can help to recognize historical architectural elements, at an adequate level of detail, and thus speed up the process of modeling of historical buildings for developing BIM models from survey data, referred to as HBIM (Historical Building Information Modeling). In this paper, we propose a DL framework for Point Cloud segmentation, which employs an improved DGCNN (Dynamic Graph Convolutional Neural Network) by adding meaningful features such as normal and colour. The approach has been applied to a newly collected DCH Dataset which is publicy available: ArCH (Architectural Cultural Heritage) Dataset. This dataset comprises 11 labeled points clouds, derived from the union of several single scans or from the integration of the latter with photogrammetric surveys. The involved scenes are both indoor and outdoor, with churches, chapels, cloisters, porticoes and loggias covered by a variety of vaults and beared by many different types of columns. They belong to different historical periods and different styles, in order to make the dataset the least possible uniform and homogeneous (in the repetition of the architectural elements) and the results as general as possible. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach.

scholarly journals Efficient Calculation Method for Tree Stem Traits from Large-Scale Point Clouds of Forest Stands

2021 ◽  
Vol 13 (13) ◽  
pp. 2476
Author(s):  
Hiroshi Masuda ◽  
Yuichiro Hiraoka ◽  
Kazuto Saito ◽  
Shinsuke Eto ◽  
Michinari Matsushita ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Random Access ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Forest Stands ◽  
Cross Sectional ◽  
Cloud Data ◽  
Wireframe Model

With the use of terrestrial laser scanning (TLS) in forest stands, surveys are now equipped to obtain dense point cloud data. However, the data range, i.e., the number of points, often reaches the billions or even higher, exceeding random access memory (RAM) limits on common computers. Moreover, the processing time often also extends beyond acceptable processing lengths. Thus, in this paper, we present a new method of efficiently extracting stem traits from huge point cloud data obtained by TLS, without subdividing or downsampling the point clouds. In this method, each point cloud is converted into a wireframe model by connecting neighboring points on the same continuous surface, and three-dimensional points on stems are resampled as cross-sectional points of the wireframe model in an out-of-core manner. Since the data size of the section points is much smaller than the original point clouds, stem traits can be calculated from the section points on a common computer. With the study method, 1381 tree stems were calculated from 3.6 billion points in ~20 min on a common computer. To evaluate the accuracy of this method, eight targeted trees were cut down and sliced at 1-m intervals; actual stem traits were then compared to those calculated from point clouds. The experimental results showed that the efficiency and accuracy of the proposed method are sufficient for practical use in various fields, including forest management and forest research.

scholarly journals Application of terrestrial laser scanning to the development and updating of the base map

2017 ◽  
Vol 66 (1) ◽  
pp. 59-71 ◽  
Author(s):  
Przemysław Klapa ◽  
Bartosz Mitka
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Best Practice ◽  
Terrestrial Laser Scanning ◽  
Spatial Location ◽  
Point Clouds ◽  
Quality Analysis ◽  
Design Engineers ◽  
Laws And Regulations

Abstract The base map provides basic information about land to individuals, companies, developers, design engineers, organizations, and government agencies. Its contents include spatial location data for control network points, buildings, land lots, infrastructure facilities, and topographic features. As the primary map of the country, it must be developed in accordance with specific laws and regulations and be continuously updated. The base map is a data source used for the development and updating of derivative maps and other large scale cartographic materials such as thematic or topographic maps. Thanks to the advancement of science and technology, the quality of land surveys carried out by means of terrestrial laser scanning (TLS) matches that of traditional surveying methods in many respects. This paper discusses the potential application of output data from laser scanners (point clouds) to the development and updating of cartographic materials, taking Poland’s base map as an example. A few research sites were chosen to present the method and the process of conducting a TLS land survey: a fragment of a residential area, a street, the surroundings of buildings, and an undeveloped area. The entire map that was drawn as a result of the survey was checked by comparing it to a map obtained from PODGiK (pol. Powiatowy Ośrodek Dokumentacji Geodezyjnej i Kartograficznej – Regional Centre for Geodetic and Cartographic Records) and by conducting a field inspection. An accuracy and quality analysis of the conducted fieldwork and deskwork yielded very good results, which provide solid grounds for predicating that cartographic materials based on a TLS point cloud are a reliable source of information about land. The contents of the map that had been created with the use of the obtained point cloud were very accurately located in space (x, y, z). The conducted accuracy analysis and the inspection of the performed works showed that high quality is characteristic of TLS surveys. The accuracy of determining the location of the various map contents has been estimated at 0.02-0.03 m. The map was developed in conformity with the applicable laws and regulations as well as with best practice requirements.

scholarly journals Data-Driven Point Cloud Objects Completion

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1514 ◽  
Author(s):  
Yang Zhang ◽  
Zhen Liu ◽  
Xiang Li ◽  
Yu Zang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Data Driven ◽  
3D Perception ◽  
Single View ◽  
Decoder Architecture ◽  
Scanned Image ◽  
2D And 3D

With the development of the laser scanning technique, it is easier to obtain 3D large-scale scene rapidly. However, many scanned objects may suffer serious incompletion caused by the scanning angles or occlusion, which has severely impacted their future usage for the 3D perception and modeling, while traditional point cloud completion methods often fails to provide satisfactory results due to the large missing parts. In this paper, by utilising 2D single-view images to infer 3D structures, we propose a data-driven Point Cloud Completion Network ( P C C N e t ), which is an image-guided deep-learning-based object completion framework. With the input of incomplete point clouds and the corresponding scanned image, the network can acquire enough completion rules through an encoder-decoder architecture. Based on an attention-based 2D-3D fusion module, the network is able to integrate 2D and 3D features adaptively according to their information integrity. We also propose a projection loss as an additional supervisor to have a consistent spatial distribution from multi-view observations. To demonstrate the effectiveness, first, the proposed P C C N e t is compared to recent generative networks and has shown more powerful 3D reconstruction abilities. Then, P C C N e t is compared to a recent point cloud completion methods, which has demonstrate that the proposed P C C N e t is able to provide satisfied completion results for objects with large missing parts.

scholarly journals Airborne Laser Scanning Point Cloud Classification Using the DGCNN Deep Learning Method

2021 ◽  
Vol 13 (5) ◽  
pp. 859
Author(s):  
Elyta Widyaningrum ◽  
Qian Bai ◽  
Marda K. Fajari ◽  
Roderik C. Lindenbergh
Keyword(s):  
Deep Learning ◽  
Urban Areas ◽  
Point Cloud ◽  
Laser Scanning ◽  
Block Size ◽  
Point Clouds ◽  
Learning Method ◽  
Cloud Classification ◽  
Indoor Scenes ◽  
Point Cloud Classification

Classification of aerial point clouds with high accuracy is significant for many geographical applications, but not trivial as the data are massive and unstructured. In recent years, deep learning for 3D point cloud classification has been actively developed and applied, but notably for indoor scenes. In this study, we implement the point-wise deep learning method Dynamic Graph Convolutional Neural Network (DGCNN) and extend its classification application from indoor scenes to airborne point clouds. This study proposes an approach to provide cheap training samples for point-wise deep learning using an existing 2D base map. Furthermore, essential features and spatial contexts to effectively classify airborne point clouds colored by an orthophoto are also investigated, in particularly to deal with class imbalance and relief displacement in urban areas. Two airborne point cloud datasets of different areas are used: Area-1 (city of Surabaya—Indonesia) and Area-2 (cities of Utrecht and Delft—the Netherlands). Area-1 is used to investigate different input feature combinations and loss functions. The point-wise classification for four classes achieves a remarkable result with 91.8% overall accuracy when using the full combination of spectral color and LiDAR features. For Area-2, different block size settings (30, 50, and 70 m) are investigated. It is found that using an appropriate block size of, in this case, 50 m helps to improve the classification until 93% overall accuracy but does not necessarily ensure better classification results for each class. Based on the experiments on both areas, we conclude that using DGCNN with proper settings is able to provide results close to production.

scholarly journals FEATURE RELEVANCE ANALYSIS FOR 3D POINT CLOUD CLASSIFICATION USING DEEP LEARNING

2019 ◽  
Vol IV-2/W5 ◽  
pp. 373-380 ◽  
Author(s):  
A. Kumar ◽  
K. Anders ◽  
L Winiwarter ◽  
B. Höfle
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Normal Vector ◽  
3D Point Cloud ◽  
Point Distribution ◽  
Spatial Point ◽  
3D Point Clouds ◽  
Normal Vectors

<p><strong>Abstract.</strong> 3D point clouds acquired by laser scanning and other techniques are difficult to interpret because of their irregular structure. To make sense of this data and to allow for the derivation of useful information, a segmentation of the points in groups, units, or classes fit for the specific use case is required. In this paper, we present a non-end-to-end deep learning classifier for 3D point clouds using multiple sets of input features and compare it with an implementation of the state-of-the-art deep learning framework PointNet++. We first start by extracting features derived from the local normal vector (normal vectors, eigenvalues, and eigenvectors) from the point cloud, and study the result of classification for different local search radii. We extract additional features related to spatial point distribution and use them together with the normal vector-based features. We find that the classification accuracy improves by up to 33% as we include normal vector features with multiple search radii and features related to spatial point distribution. Our method achieves a mean Intersection over Union (mIoU) of 94% outperforming PointNet++’s Multi Scale Grouping by up to 12%. The study presents the importance of multiple search radii for different point cloud features for classification in an urban 3D point cloud scene acquired by terrestrial laser scanning.</p>

scholarly journals AUTOMATED COARSE REGISTRATION OF POINT CLOUDS IN 3D URBAN SCENES USING VOXEL BASED PLANE CONSTRAINT

2017 ◽  
Vol IV-2/W4 ◽  
pp. 185-191 ◽  
Author(s):  
Y. Xu ◽  
R. Boerner ◽  
W. Yao ◽  
L. Hoegner ◽  
U. Stilla
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Coordinate Frame ◽  
Small Individual ◽  
Planar Surfaces ◽  
Coarse Registration ◽  
Transformation Parameters ◽  
Normal Vectors

For obtaining a full coverage of 3D scans in a large-scale urban area, the registration between point clouds acquired via terrestrial laser scanning (TLS) is normally mandatory. However, due to the complex urban environment, the automatic registration of different scans is still a challenging problem. In this work, we propose an automatic marker free method for fast and coarse registration between point clouds using the geometric constrains of planar patches under a voxel structure. Our proposed method consists of four major steps: the voxelization of the point cloud, the approximation of planar patches, the matching of corresponding patches, and the estimation of transformation parameters. In the voxelization step, the point cloud of each scan is organized with a 3D voxel structure, by which the entire point cloud is partitioned into small individual patches. In the following step, we represent points of each voxel with the approximated plane function, and select those patches resembling planar surfaces. Afterwards, for matching the corresponding patches, a RANSAC-based strategy is applied. Among all the planar patches of a scan, we randomly select a planar patches set of three planar surfaces, in order to build a coordinate frame via their normal vectors and their intersection points. The transformation parameters between scans are calculated from these two coordinate frames. The planar patches set with its transformation parameters owning the largest number of coplanar patches are identified as the optimal candidate set for estimating the correct transformation parameters. The experimental results using TLS datasets of different scenes reveal that our proposed method can be both effective and efficient for the coarse registration task. Especially, for the fast orientation between scans, our proposed method can achieve a registration error of less than around 2 degrees using the testing datasets, and much more efficient than the classical baseline methods.

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