Point Cloud Reconstruction Based on SVR

2011 ◽  
Vol 403-408 ◽  
pp. 3267-3270
Author(s):  
Jin Guang Sun ◽  
Jun Tao Wang ◽  
Xin Nian Yang ◽  
Yang Li
Keyword(s):  
Support Vector Regression ◽  
Point Cloud ◽  
Reconstruction Algorithm ◽  
Surface Expression ◽  
Experimental Results ◽  
Support Vector ◽  
Implicit Function ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Marching Cube Algorithm

This paper presents a point cloud reconstruction algorithm which based on SVR(support vector regression) . Firstly, the point cloud data pre-processing, filter out noise points. Then train the point by SVR , and we can get the function of surface expression. Finally, using the Marching Cube algorithm to visualize the implicit function. Experimental results show that the algorithm is more robust and more efficient.

scholarly journals Scattered Point Cloud Data Reconstruction Algorithm Based on Local Convexity

2020 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Point Cloud ◽  
Reconstruction Algorithm ◽  
Large Error ◽  
Small Error ◽  
Data Reconstruction ◽  
Point Cloud Data ◽  
Local Convexity ◽  
Cloud Data ◽  
Time Consumption ◽  
Local Connection

Point cloud data reconstruction is the basis of point cloud data processing. The reconstruction effect has a great impact on application. For the problems of low precision, large error, and high time consumption of the current scattered point cloud data reconstruction algorithm, a new algorithm of scattered point cloud data reconstruction based on local convexity is proposed in this paper. Firstly, according to surface variation based on local outlier factor (SVLOF), the noise points of point cloud data are divided into near outlier and far outlier, and filtered for point cloud data preprocessing. Based on this, the algorithm based on local convexity is improved. The method of constructing local connection point set is used to replace triangulation to analyze the relationship of neighbor points. The connection part identification method is used for data reconstruction. Experimental results show that, the proposed method can reconstruct the scattered point cloud data accurately, with high precision, small error and low time consumption.

scholarly journals CLASSIFICATION OF LIDAR DATA FOR GENERATING A HIGH-PRECISION ROADWAY MAP

2016 ◽  
Vol XLI-B3 ◽  
pp. 251-254 ◽  
Author(s):  
J. Jeong ◽  
I. Lee
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Autonomous Driving ◽  
Learning Objects ◽  
Support Vector ◽  
Feature Descriptor ◽  
Point Cloud Data ◽  
Cloud Data ◽  
On The Road

Generating of a highly precise map grows up with development of autonomous driving vehicles. The highly precise map includes a precision of centimetres level unlike an existing commercial map with the precision of meters level. It is important to understand road environments and make a decision for autonomous driving since a robust localization is one of the critical challenges for the autonomous driving car. The one of source data is from a Lidar because it provides highly dense point cloud data with three dimensional position, intensities and ranges from the sensor to target. In this paper, we focus on how to segment point cloud data from a Lidar on a vehicle and classify objects on the road for the highly precise map. In particular, we propose the combination with a feature descriptor and a classification algorithm in machine learning. Objects can be distinguish by geometrical features based on a surface normal of each point. To achieve correct classification using limited point cloud data sets, a Support Vector Machine algorithm in machine learning are used. Final step is to evaluate accuracies of obtained results by comparing them to reference data The results show sufficient accuracy and it will be utilized to generate a highly precise road map.

scholarly journals VOXEL-BASED EXTRACTION OF INDIVIDUAL PYLONS AND WIRES FROM LIDAR POINT CLOUD DATA

2019 ◽  
Vol IV-4/W8 ◽  
pp. 91-98 ◽  
Author(s):  
N. Munir ◽  
M. Awrangjeb ◽  
B. Stantic ◽  
G. Lu ◽  
S. Islam
Keyword(s):  
Vertical Profile ◽  
Point Cloud ◽  
Support Vector ◽  
Svm Classifier ◽  
Lidar Data ◽  
Point Cloud Data ◽  
Cloud Data ◽  
3D Objects ◽  
Voxel Grid ◽  
Power Line Corridor

<p><strong>Abstract.</strong> Extraction of individual pylons and wires is important for modelling of 3D objects in a power line corridor (PLC) map. However, the existing methods mostly classify points into distinct classes like pylons and wires, but hardly into individual pylons or wires. The proposed method extracts standalone pylons, vegetation and wires from LiDAR data. The extraction of individual objects is needed for a detailed PLC mapping. The proposed approach starts off with the separation of ground and non ground points. The non-ground points are then classified into vertical (e.g., pylons and vegetation) and non-vertical (e.g., wires) object points using the vertical profile feature (VPF) through the binary support vector machine (SVM) classifier. Individual pylons and vegetation are then separated using their shape and area properties. The locations of pylons are further used to extract the span points between two successive pylons. Finally, span points are voxelised and alignment properties of wires in the voxel grid is used to extract individual wires points. The results are evaluated on dataset which has multiple spans with bundled wires in each span. The evaluation results show that the proposed method and features are very effective for extraction of individual wires, pylons and vegetation with 99% correctness and 98% completeness.</p>

scholarly journals Scattered Point Cloud Data Reconstruction Algorithm Based on Local Convexity

2020 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Point Cloud ◽  
Reconstruction Algorithm ◽  
Large Error ◽  
Small Error ◽  
Data Reconstruction ◽  
Point Cloud Data ◽  
Local Convexity ◽  
Cloud Data ◽  
Time Consumption ◽  
Local Connection

Point cloud data reconstruction is the basis of point cloud data processing. The reconstruction effect has a great impact on application. For the problems of low precision, large error, and high time consumption of the current scattered point cloud data reconstruction algorithm, a new algorithm of scattered point cloud data reconstruction based on local convexity is proposed in this paper. Firstly, according to surface variation based on local outlier factor (SVLOF), the noise points of point cloud data are divided into near outlier and far outlier, and filtered for point cloud data preprocessing. Based on this, the algorithm based on local convexity is improved. The method of constructing local connection point set is used to replace triangulation to analyze the relationship of neighbor points. The connection part identification method is used for data reconstruction. Experimental results show that, the proposed method can reconstruct the scattered point cloud data accurately, with high precision, small error and low time consumption.

scholarly journals BIM RECONSTRUCTION: AUTOMATED PROCEDURAL MODELING FROM POINT CLOUD DATA

2019 ◽  
Vol XLII-2/W17 ◽  
pp. 53-60
Author(s):  
M. Bassier ◽  
L. Mattheuwsen ◽  
M. Vergauwen
Keyword(s):  
Point Cloud ◽  
Reconstruction Algorithm ◽  
Information Modeling ◽  
Data Sets ◽  
Point Cloud Data ◽  
Ongoing Research ◽  
Cloud Data ◽  
Structure Information ◽  
Wide Range ◽  
Building Information

Abstract. The reconstruction of Building Information Modeling objects for as-built modeling is currently the subject of ongoing research. A popular method is to extract structure information from point cloud data to create a set of parametric objects. This requires the interpretation of the point cloud data which currently is a manual and labor intensive procedure. Automated processes have to cope with excessive occlusions and clutter in the data sets. To create an as-built BIM, it is vital to reconstruct the building’s structure i.e. wall geometry prior to the reconstruction of other objects.In this work, a novel method is presented to automatically reconstruct as-built BIM for generic buildings. We presented an unsupervised method that procedurally models the geometry of the walls based on point cloud data. A bottom-up process is defined where consecutively higher level information is extracted from the point cloud data using pre-trained machine learning models. Prior to the reconstruction, the data is segmented, classified and clustered to retrieve all the available observations of the walls. The resulting geometry is processed by the reconstruction algorithm. First, the necessary information is extracted from the observations for the creation of parametric solid objects. Subsequently, the final walls are created by updating their topology. The method is tested on a variety of scenes and shows promising results to reliably and accurately create as-built models. The accuracy of the generated geometry is similar to the precision of expert modelers. A key advantage is that that the algorithm creates Revit and Rhino native objects which makes the geometry directly applicable to a wide range of applications.

scholarly journals Scattered Point Cloud Data Reconstruction Algorithm Based on Local Convexity

2020 ◽  
Vol 2 (2) ◽  
pp. 141-149
Author(s):  
Sorush Niknamian
Keyword(s):  
Point Cloud ◽  
Reconstruction Algorithm ◽  
Large Error ◽  
Small Error ◽  
Data Reconstruction ◽  
Point Cloud Data ◽  
Local Convexity ◽  
Cloud Data ◽  
Time Consumption ◽  
Local Connection

Point cloud data reconstruction is the basis of point cloud data processing. The reconstruction effect has a great impact on application. For the problems of low precision, large error, and high time consumption of the current scattered point cloud data reconstruction algorithm, a new algorithm of scattered point cloud data reconstruction based on local convexity is proposed in this paper. Firstly, according to surface variation based on local outlier factor (SVLOF), the noise points of point cloud data are divided into near outlier and far outlier, and filtered for point cloud data preprocessing. Based on this, the algorithm based on local convexity is improved. The method of constructing local connection point set is used to replace triangulation to analyze the relationship of neighbor points. The connection part identification method is used for data reconstruction. Experimental results show that, the proposed method can reconstruct the scattered point cloud data accurately, with high precision, small error and low time consumption.

scholarly journals Comparison of 2D and 3D wall reconstruction algorithms from point cloud data for as-built BIM

2020 ◽  
Vol 25 ◽  
pp. 173-192 ◽  
Author(s):  
Maarten Bassier ◽  
Meisam Yousefzadeh ◽  
Maarten Vergauwen
Keyword(s):  
Point Cloud ◽  
Real Life ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Point Cloud Data ◽  
Empirical Comparison ◽  
Ongoing Research ◽  
Cloud Data ◽  
2D And 3D ◽  
Wall Geometry

As-built Building Information Models (BIMs) are becoming increasingly popular in the Architectural, Engineering, Construction, Owner and Operator (AECOO) industry. These models reflect the state of the building up to as-built conditions. The production of these models for existing buildings with no prior BIM includes the segmentation and classification of point cloud data and the reconstruction of the BIM objects. The automation of this process is a must since the manual Scan-to-BIM procedure is both time-consuming and error prone. However, the automated reconstruction from point cloud data is still ongoing research with both 2D and 3D approaches being proposed. There currently is a gap in the literature concerning the quality assessment of the created entities. In this research, we present the empirical comparison of both strategies with respect to existing specifications. A 3D and a 2D reconstruction method are implemented and tested on a real life test case. The experiments focus on the reconstruction of the wall geometry from unstructured point clouds as it forms the basis of the model. Both presented approaches are unsupervised methods that segment, classify and create generic wall elements. The first method operates on the 3D point cloud itself and consists of a general approach for the segmentation and classification and a class-specific reconstruction algorithm for the wall geometry. The point cloud is first segmented into planar clusters, after which a Random Forests classifier is used with geometric and contextual features for the semantic labelling. The final wall geometry is created based on the 3D point clusters representing the walls. The second method is an efficient Manhattan-world scene reconstruction algorithm that simultaneously segments and classifies the point cloud based on point feature histograms. The wall reconstruction is considered an instance of image segmentation by representing the data as 2D raster images. Both methods have promising results towards the reconstruction of wall geometry of multi-story buildings. The experiments report that over 80% of the walls were correctly segmented by both methods. Furthermore, the reconstructed geometry is conform Level-of-Accuracy 20 for 88% of the data by the first method and for 55% by the second method despite the Manhattan-world scene assumption. The empirical comparison showcases the fundamental differences in both strategies and will support the further development of these methods.

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