Registration of Point Clouds Based on Differential Geometry of Surface’s Feature

2011 ◽  
Vol 101-102 ◽  
pp. 232-235
Author(s):  
Xue Сhang Zhang ◽  
Xue Jun Gao
Keyword(s):  
Differential Geometry ◽  
Point Cloud ◽  
Optimal Algorithm ◽  
Point Clouds ◽  
View Point ◽  
Geometric Information ◽  
Point To Point

A method for accurate registration on point clouds is presented in the paper. Manual alignment or the use of landmarks is avoided in the process of multi-view point clouds. Firstly, the differential geometric information is extracted from the point clouds. The extended Gaussian sphere and combination features are used to define the corresponding points of crude alignment. Secondly, the optimal algorithm，the point-to-point Iterated Closest Point, is applied to the accurate registration on point clouds. Thus, the complete point cloud can be obtained in the method.

scholarly journals Automated Measurement of Heart Girth for Pigs Using Two Kinect Depth Sensors

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3848
Author(s):  
Xinyue Zhang ◽  
Gang Liu ◽  
Ling Jing ◽  
Siyao Chen
Keyword(s):  
Mirror Symmetry ◽  
Point Cloud ◽  
Point Clouds ◽  
Depth Image ◽  
View Point ◽  
Important Indicator ◽  
Depth Sensors ◽  
Starting Point ◽  
Feature Based ◽  
Girth Measurement

The heart girth parameter is an important indicator reflecting the growth and development of pigs that provides critical guidance for the optimization of healthy pig breeding. To overcome the heavy workloads and poor adaptability of traditional measurement methods currently used in pig breeding, this paper proposes an automated pig heart girth measurement method using two Kinect depth sensors. First, a two-view pig depth image acquisition platform is established for data collection; the two-view point clouds after preprocessing are registered and fused by feature-based improved 4-Point Congruent Set (4PCS) method. Second, the fused point cloud is pose-normalized, and the axillary contour is used to automatically extract the heart girth measurement point. Finally, this point is taken as the starting point to intercept the circumferential perpendicular to the ground from the pig point cloud, and the complete heart girth point cloud is obtained by mirror symmetry. The heart girth is measured along this point cloud using the shortest path method. Using the proposed method, experiments were conducted on two-view data from 26 live pigs. The results showed that the heart girth measurement absolute errors were all less than 4.19 cm, and the average relative error was 2.14%, which indicating a high accuracy and efficiency of this method.

scholarly journals MVPNet: Multi-View Point Regression Networks for 3D Object Reconstruction from A Single Image

2019 ◽  
Vol 33 ◽  
pp. 8949-8956 ◽  
Author(s):  
Jinglu Wang ◽  
Bo Sun ◽  
Yan Lu
Keyword(s):  
Point Cloud ◽  
Image Plane ◽  
Point Clouds ◽  
Projective Planes ◽  
Network Architectures ◽  
View Point ◽  
Single Image ◽  
Dense Point ◽  
Dependent Point ◽  
3D Surfaces

In this paper, we address the problem of reconstructing an object’s surface from a single image using generative networks. First, we represent a 3D surface with an aggregation of dense point clouds from multiple views. Each point cloud is embedded in a regular 2D grid aligned on an image plane of a viewpoint, making the point cloud convolution-favored and ordered so as to fit into deep network architectures. The point clouds can be easily triangulated by exploiting connectivities of the 2D grids to form mesh-based surfaces. Second, we propose an encoder-decoder network that generates such kind of multiple view-dependent point clouds from a single image by regressing their 3D coordinates and visibilities. We also introduce a novel geometric loss that is able to interpret discrepancy over 3D surfaces as opposed to 2D projective planes, resorting to the surface discretization on the constructed meshes. We demonstrate that the multi-view point regression network outperforms state-of-the-art methods with a significant improvement on challenging datasets.

scholarly journals Accuracy assessment of building point clouds automatically generated from iphone images

2014 ◽  
Vol XL-5 ◽  
pp. 547-552 ◽  
Author(s):  
B. Sirmacek ◽  
R. Lindenbergh
Keyword(s):  
Measurement Errors ◽  
Point Cloud ◽  
Model Updating ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Point Clouds ◽  
3D Models ◽  
Map Updating ◽  
Cloud Points ◽  
Point To Point

Low-cost sensor generated 3D models can be useful for quick 3D urban model updating, yet the quality of the models is questionable. In this article, we evaluate the reliability of an automatic point cloud generation method using multi-view iPhone images or an iPhone video file as an input. We register such automatically generated point cloud on a TLS point cloud of the same object to discuss accuracy, advantages and limitations of the iPhone generated point clouds. For the chosen example showcase, we have classified 1.23% of the iPhone point cloud points as outliers, and calculated the mean of the point to point distances to the TLS point cloud as 0.11 m. Since a TLS point cloud might also include measurement errors and noise, we computed local noise values for the point clouds from both sources. Mean (μ) and standard deviation (&amp;sigma;) of roughness histograms are calculated as (μ<sub>1</sub> = 0.44 m., &amp;sigma;<sub>1</sub> = 0.071 m.) and (μ<sub>2</sub> = 0.025 m., &amp;sigma;<sub>2</sub> = 0.037 m.) for the iPhone and TLS point clouds respectively. Our experimental results indicate possible usage of the proposed automatic 3D model generation framework for 3D urban map updating, fusion and detail enhancing, quick and real-time change detection purposes. However, further insights should be obtained first on the circumstances that are needed to guarantee a successful point cloud generation from smartphone images.

scholarly journals Dynamic Point Cloud Compression Based on Projections, Surface Reconstruction and Video Compression

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 197
Author(s):  
Emil Dumic ◽  
Anamaria Bjelopera ◽  
Andreas Nüchter
Keyword(s):  
Surface Reconstruction ◽  
Video Compression ◽  
Point Cloud ◽  
Voronoi Diagrams ◽  
Reconstruction Algorithm ◽  
Point Clouds ◽  
Algorithm Comparison ◽  
Point To Point ◽  
Original Point

In this paper we will present a new dynamic point cloud compression based on different projection types and bit depth, combined with the surface reconstruction algorithm and video compression for obtained geometry and texture maps. Texture maps have been compressed after creating Voronoi diagrams. Used video compression is specific for geometry (FFV1) and texture (H.265/HEVC). Decompressed point clouds are reconstructed using a Poisson surface reconstruction algorithm. Comparison with the original point clouds was performed using point-to-point and point-to-plane measures. Comprehensive experiments show better performance for some projection maps: cylindrical, Miller and Mercator projections.

scholarly journals Methodology for remote assessment of pavement distresses from point cloud analysis

2021 ◽  
Author(s):  
Ernest Berney ◽  
Naveen Ganesh ◽  
Andrew Ward ◽  
J. Newman ◽  
John Rushing
Keyword(s):  
Point Cloud ◽  
Surface Condition ◽  
Three Dimensional ◽  
Point Clouds ◽  
Pavement Surface ◽  
Cloud Models ◽  
Rapid Generation ◽  
Point To Point ◽  
Point Cloud Analysis ◽  
Cloud Analysis

The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-based sensors. ERDC-developed algorithms automatically discretize the pavement surface into cross- and grid-based sections to identify physical surface distresses such as depressions, ruts, and cracks. Depressions can be sized from the point-to-point distances bounding each depression, and surface roughness is determined based on the point heights along a given cross section. Noted distresses are exported to a distress map file containing only the distress points and their locations for later visualization and quality control along with classification and quantification. Further research and automation into point cloud analysis is ongoing with the goal of enabling Soldiers with limited training the capability to rapidly assess pavement surface condition from a remote platform.

scholarly journals PLANAR CONSTRAINTS FOR AN IMPROVED UAV-IMAGE-BASED DENSE POINT CLOUD GENERATION

2015 ◽  
Vol XL-1/W4 ◽  
pp. 269-274 ◽  
Author(s):  
F. He ◽  
A. Habib ◽  
A. Al-Rawabdeh
Keyword(s):  
Point Cloud ◽  
Experimental Tests ◽  
Point Clouds ◽  
Geometric Information ◽  
Planar Surfaces ◽  
3D Point Clouds ◽  
Dense Image ◽  
Dense Point ◽  
Uav Image

In this paper, we proposed a new refinement procedure for the semi-global dense image matching. In order to remove outliers and improve the disparity image derived from the semi-global algorithm, both the local smoothness constraint and point cloud segments are utilized. Compared with current refinement technique, which usually assumes the correspondences between planar surfaces and 2D image segments, our proposed approach can effectively deal with object with both planar and curved surfaces. Meanwhile, since 3D point clouds contain more precise geometric information regarding to the reconstructed objects, the planar surfaces identified in our approach can be more accurate. In order to illustrate the feasibility of our approach, several experimental tests are conducted on both Middlebury test and real UAV-image datasets. The results demonstrate that our approach has a good performance on improving the quality of the derived dense image-based point cloud.

scholarly journals Wood–leaf Classification of Tree Point Cloud Based on Intensity and Geometric Information

2021 ◽  
Vol 13 (20) ◽  
pp. 4050
Author(s):  
Jingqian Sun ◽  
Pei Wang ◽  
Zhiyong Gao ◽  
Zichu Liu ◽  
Yaxin Li ◽  
...  
Keyword(s):  
Point Cloud ◽  
High Speed ◽  
Laser Scanning ◽  
Structural Parameters ◽  
Point Clouds ◽  
Neighborhood Density ◽  
Geometric Information ◽  
Classification Technique ◽  
Manual Classification

Terrestrial laser scanning (TLS) can obtain tree point clouds with high precision and high density. The efficient classification of wood points and leaf points is essential for the study of tree structural parameters and ecological characteristics. Using both intensity and geometric information, we present an automated wood–leaf classification with a three-step classification and wood point verification. The tree point cloud was classified into wood points and leaf points using intensity threshold, neighborhood density and voxelization successively, and was then verified. Twenty-four willow trees were scanned using the RIEGL VZ-400 scanner. Our results were compared with the manual classification results. To evaluate the classification accuracy, three indicators were introduced into the experiment: overall accuracy (OA), Kappa coefficient (Kappa), and Matthews correlation coefficient (MCC). The ranges of OA, Kappa, and MCC of our results were from 0.9167 to 0.9872, 0.7276 to 0.9191, and 0.7544 to 0.9211, respectively. The average values of OA, Kappa, and MCC were 0.9550, 0.8547, and 0.8627, respectively. The time costs of our method and another were also recorded to evaluate the efficiency. The average processing time was 1.4 seconds per million points for our method. The results show that our method represents a potential wood–leaf classification technique with the characteristics of automation, high speed, and good accuracy.

scholarly journals MODELLING A CELL TOWER USING SFM: AUTOMATED DETECTION OF STRUCTURAL ELEMENTS FROM SKELETON EXTRACTION ON A POINT CLOUD

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 399-406
Author(s):  
M. Deidda ◽  
A. Pala ◽  
G. Sanna
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Skeleton Extraction ◽  
Finite Elements Analysis ◽  
Geometric Information ◽  
A Cell ◽  
The Many ◽  
Geometric Elements ◽  
Cell Tower

Abstract. The surveying and management of telecommunication towers poses a series of engineering challenges. Not only they must be regularly inspected for the purpose of checking for issues that require maintenance interventions, but they are often sub-let by their owners to communication companies, requiring a survey of the many (several thousand per company) installed appliances to check that they respect the established contracts. This requires a surveying methodology that is fast and possibly automated. Photogrammetric techniques using UAV-mounted cameras seem to offer a solution that is both suitable and economical. Our research team was asked to evaluate whether, from the information acquired by small drones it was possible to obtain geometric information on the structure, with what degree of accuracy and what level of detail. The workflow of this process is naturally articulated in three steps: the acquisition, the construction of the point cloud, and the extraction of geometries. The case study is a tower carrying antennas owned by several operators and placed in the industrial district of Cagliari. The article examines the problems found in modelling such structures using point clouds derived from the Structure-from-Motion technique, in order to obtain a model of nodes and beams suitable for the reconstruction of the structure’s geometric elements, and possibly for a finite elements analysis or for populating GIS and BIM, either automatically or with minimal user intervention. In order to achieve this, we have used voxelization and skeleton extraction algorithms to obtain a 3D graph of the structure. The analysis of the results was carried out by varying the parameters relating to the voxel size, which defines the resolution, and the density of the points contained inside each voxel.

3-D Reconstruction of Image Sequence Based on Independent Three-View

2014 ◽  
Vol 989-994 ◽  
pp. 3844-3850 ◽  
Author(s):  
Cong Li ◽  
Hong Rui Zhao ◽  
Gang Fu
Keyword(s):  
Rigid Body ◽  
Coordinate System ◽  
Fundamental Matrix ◽  
Point Cloud ◽  
Point Clouds ◽  
Image Sequence ◽  
Reconstruction Method ◽  
View Point ◽  
Experiment Method ◽  
Space Coordinate

In order to realize efficient and stable 3-D reconstruction of image sequence, a novel 3-D reconstruction method is proposed based on independent three-view in this paper. Firstly, three-view is used as reconstruction unit. Consequently, robust fundamental matrix is calculated under the constraint of three-view. Then 3-D point cloud of 3-D scene corresponding to this three-view is rebuilt by triangulation. Because three-view point cloud is rigid body and local stable, 3-D point clouds of whole image sequence are unified to the same space coordinate system via calculating and using transformation parameters between adjacent three-view. Finally 3-D point cloud reconstruction of whole image sequence is achieved. As shown in experiment, method presented in this paper is simple, efficient and reliable.

scholarly journals POINT CLOUD CLASSIFICATION BY FUSING SUPERVOXEL SEGMENTATION WITH MULTI-SCALE FEATURES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 919-925
Author(s):  
W. Ao ◽  
L. Wang ◽  
J. Shan
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Geometric Information ◽  
Multi Scale ◽  
Cloud Classification ◽  
Spatial Consistency ◽  
Class Labels ◽  
Point Cloud Classification ◽  
Robust To Noise

<p><strong>Abstract.</strong> Point cloud classification is quite a challenging task due to the existence of noises, occlusion and various object types and sizes. Currently, the commonly used statistics-based features cannot accurately characterize the geometric information of a point cloud. This limitation often leads to feature confusion and classification mistakes (e.g., points of building corners and vegetation always share similar statistical features in a local neighbourhood, such as curvature, sphericity, etc). This study aims at solving this problem by leveraging the advantage of both the supervoxel segmentation and multi-scale features. For each point, its multi-scale features within different radii are extracted. Simultaneously, the point cloud is partitioned into simple supervoxel segments. After that, the class probability of each point is predicted by the proposed SegMSF approach that combines multi-scale features with the supervoxel segmentation results. At the end, the effect of data noises is supressed by using a global optimization that encourages spatial consistency of class labels. The proposed method is tested on both airborne laser scanning (ALS) and mobile laser scanning (MLS) point clouds. The experimental results demonstrate that the proposed method performs well in terms of classifying objects of different scales and is robust to noise.</p>

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