scholarly journals Iterative K-Closest Point Algorithms for Colored Point Cloud Registration

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5331
Author(s):  
Ouk Choi ◽  
Min-Gyu Park ◽  
Youngbae Hwang
Keyword(s):  
Real World ◽  
Point Cloud ◽  
Point Clouds ◽  
The Other ◽  
Iterative Closest Point ◽  
Depth Images ◽  
The Cost ◽  
Soft Matching ◽  
Colored Point ◽  
Refinement Algorithm

We present two algorithms for aligning two colored point clouds. The two algorithms are designed to minimize a probabilistic cost based on the color-supported soft matching of points in a point cloud to their K-closest points in the other point cloud. The first algorithm, like prior iterative closest point algorithms, refines the pose parameters to minimize the cost. Assuming that the point clouds are obtained from RGB-depth images, our second algorithm regards the measured depth values as variables and minimizes the cost to obtain refined depth values. Experiments with our synthetic dataset show that our pose refinement algorithm gives better results compared to the existing algorithms. Our depth refinement algorithm is shown to achieve more accurate alignments from the outputs of the pose refinement step. Our algorithms are applied to a real-world dataset, providing accurate and visually improved results.

scholarly journals Score Fusion of 3DLBP and ACDN+P for 3D Face Recognition

2016 ◽  
Vol 2 (1) ◽  
pp. 34
Author(s):  
João Baptista Cardia ◽  
Aparecido Nilceu Marana
Keyword(s):  
Face Recognition ◽  
Everyday Life ◽  
Point Cloud ◽  
The Other ◽  
Iterative Closest Point ◽  
3D Face Recognition ◽  
Depth Images ◽  
The Face ◽  
Score Fusion

Many situations of our everyday life require our identification. Biometrics-based methods, besides allowing such identification, can help to prevent frauds. Among several biometrics features, face is one of the most popular due to its intrinsic and important properties, such as universality, acceptability, lowcosts, and covert identification. On the other hand, the traditional automatic face recognition methods based on 2D features can not properly deal with some very frequent challenges, such as occlusion, illumination and pose variations. In this paper we propose a new method for face recognition based on the fusion of 3D low-level local features, ACDN+P and 3DLBP, using depth images captured by cheap Kinect V1 sensors. In order to improve the low quality of the point cloud provided by such devices, Symmetric Filling, Iterative Closest Point, and Savitzky-Golay Filter are used in the preprocessing stage of the proposed method. Experimental results obtained on EURECOM Kinect dataset showed that the proposed method can improve the face recognition rates.

scholarly journals OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1563
Author(s):  
Ruibing Wu ◽  
Ziping Yu ◽  
Donghong Ding ◽  
Qinghua Lu ◽  
Zengxi Pan ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Point Cloud ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Multiple Point ◽  
Icp Algorithm ◽  
Registration Algorithm ◽  
Cloud Models ◽  
Comparison Results ◽  
Wire Arc Additive Manufacturing

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

scholarly journals ITERATIVE CLOSEST POINT ALGORITHM FOR ACCURATE REGISTRATION OF COARSELY REGISTERED POINT CLOUDS WITH CITYGML MODELS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 201-208 ◽  
Author(s):  
S. Goebbels ◽  
R. Pohle-Fröhlich ◽  
P. Pricken
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Iteration Step ◽  
Iterative Closest Point ◽  
Target Point ◽  
Standard Tool ◽  
Iterative Closest Point Algorithm ◽  
Test Scenarios ◽  
Plane Mode ◽  
City Models

<p><strong>Abstract.</strong> The Iterative Closest Point algorithm (ICP) is a standard tool for registration of a source to a target point cloud. In this paper, ICP in point-to-plane mode is adopted to city models that are defined in CityGML. With this new point-to-model version of the algorithm, a coarsely registered photogrammetric point cloud can be matched with buildings’ polygons to provide, e.g., a basis for automated 3D facade modeling. In each iteration step, source points are projected to these polygons to find correspondences. Then an optimization problem is solved to find an affine transformation that maps source points to their correspondences as close as possible. Whereas standard ICP variants do not perform scaling, our algorithm is capable of isotropic scaling. This is necessary because photogrammetric point clouds obtained by the structure from motion algorithm typically are scaled randomly. Two test scenarios indicate that the presented algorithm is faster than ICP in point-to-plane mode on sampled city models.</p>

Point Clouds Registration by Using Depth Images

2014 ◽  
Vol 513-517 ◽  
pp. 4193-4196
Author(s):  
Wen Bao Qiao ◽  
Ming Guo ◽  
Jun Jie Liu
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Scale Space ◽  
Target Object ◽  
Icp Algorithm ◽  
Depth Images ◽  
Curvature Scale Space ◽  
Initial Location ◽  
Characteristic Points

In this paper, we propose an efficient way to produce an initial transposed matrix for two point clouds, which can effectively avoid the drawback of local optimism caused by using standard Iterative Closest Points (ICP)[ algorithm when matching two point clouds. In our approach, the correspondences used to calculate the transposed matrix are confirmed before the point cloud forms. We use the depth images which have been carefully target-segmented to find the boundaries of the shapes that reflect different views of the same target object. Then each contour is affected by curvature scale space (CSS)[ method to find a sequence of characteristic points. After that, our method is applied on these characteristic points to find the most matching pairs of points. Finally, we convert the matched characteristic points to 3D points, and the correspondences are there being confirmed. We can use them to compute an initial transposed matrix to tell the computer which part of the first point cloud should be matched to the second. In this way, we put the two point clouds in a correct initial location, so that the local optimism of ICP and its variations can be excluded.

scholarly journals TOWARDS RADIOMETRICAL ALIGNMENT OF 3D POINT CLOUDS

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 419-424 ◽  
Author(s):  
H. A. Lauterbach ◽  
D. Borrmann ◽  
A. Nüchter
Keyword(s):  
Point Cloud ◽  
Dynamic Range ◽  
Point Clouds ◽  
Color Information ◽  
Lighting Conditions ◽  
3D Point Clouds ◽  
Laser Scanners ◽  
3D Scans ◽  
Colored Point

3D laser scanners are typically not able to collect color information. Therefore coloring is often done by projecting photos of an additional camera to the 3D scans. The capturing process is time consuming and therefore prone to changes in the environment. The appearance of the colored point cloud is mainly effected by changes of lighting conditions and corresponding camera settings. In case of panorama images these exposure variations are typically corrected by radiometrical aligning the input images to each other. In this paper we adopt existing methods for panorama optimization in order to correct the coloring of point clouds. Therefore corresponding pixels from overlapping images are selected by using geometrically closest points of the registered 3D scans and their neighboring pixels in the images. The dynamic range of images in raw format allows for correction of large exposure differences. Two experiments demonstrate the abilities of the approach.

scholarly journals Recognition of Fingerspelling Sequences in Polish Sign Language Using Point Clouds Obtained from Depth Images

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1078 ◽  
Author(s):  
Dawid Warchoł ◽  
Tomasz Kapuściński ◽  
Marian Wysocki
Keyword(s):  
Point Cloud ◽  
Markov Models ◽  
Hidden Markov ◽  
Shape Representation ◽  
Depth Map ◽  
Point Clouds ◽  
Shape Functions ◽  
Depth Images ◽  
Hand Shape ◽  
Transition Models

The paper presents a method for recognizing sequences of static letters of the Polish finger alphabet using the point cloud descriptors: viewpoint feature histogram, eigenvalues-based descriptors, ensemble of shape functions, and global radius-based surface descriptor. Each sequence is understood as quick highly coarticulated motions, and the classification is performed by networks of hidden Markov models trained by transitions between postures corresponding to particular letters. Three kinds of the left-to-right Markov models of the transitions, two networks of the transition models—independent and dependent on a dictionary—as well as various combinations of point cloud descriptors are examined on a publicly available dataset of 4200 executions (registered as depth map sequences) prepared by the authors. The hand shape representation proposed in our method can also be applied for recognition of hand postures in single frames. We confirmed this using a known, challenging American finger alphabet dataset with about 60,000 depth images.

A new flexible laser beam profiler for the inspection of train wheels

2020 ◽  
pp. 095440972092200
Author(s):  
M Torabi ◽  
SM Mousavi G ◽  
D Younesian
Keyword(s):  
Laser Beam ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Calibration Method ◽  
The Other ◽  
Geometrical Parameters ◽  
Prototype System ◽  
Perpendicular Plane ◽  
Wheel Profile

In this paper, a flexible laser beam profiler is proposed to easily measure the profile of a train wheel for railway inspection. It only requires two laser beams (together and in parallel) to obtain two three-dimensional point-clouds based on the laser triangulation principle. Either the laser beam profiler or the wheel can be freely moved. The motion need not be known. The wheel profile will be obtained in two steps. First, the wheel axis position and orientation are obtained by minimizing the distance between one of the point-clouds and the other translated point-cloud, and translation is defined as a rotation of any point on the point-cloud around the wheel axis until it lies on the other point-cloud's laser plane. In the second step, the wheel profile is extracted by selecting one of the point-clouds and rotating it about the wheel axis and by finding the intersection of rotating points and a perpendicular plane, the perpendicular plane is defined as any arbitrary plane which passes through the wheel axis. This method is useful particularly for obtaining geometrical parameters of a wheel such as flange height, flange slope and flange thickness. In order to commission the proposed method, a prototype system was designed and manufactured. The performance of the system, evaluated in different circumstances, shows a measurement error of up to 2%. Compared with classical methods utilizing a caliper or those which use expensive equipment or additional parts such as reference guides, the proposed method is easy to use and flexible. Also, a novel calibration method is utilized to calibrate the system accurately and freely.

scholarly journals Non-Temporal Point Cloud Analysis for Surface Damage in Civil Structures

2019 ◽  
Vol 8 (12) ◽  
pp. 527 ◽  
Author(s):  
Mohammad Ebrahim Mohammadi ◽  
Richard L. Wood ◽  
Christine E. Wittich
Keyword(s):  
Damage Detection ◽  
Real World ◽  
Point Cloud ◽  
Detection Method ◽  
False Positive Rate ◽  
Point Clouds ◽  
Surface Damage ◽  
Normal Surface ◽  
Synthetic Datasets

Assessment and evaluation of damage in civil infrastructure is most often conducted visually, despite its subjectivity and qualitative nature in locating and verifying damaged areas. This study aims to present a new workflow to analyze non-temporal point clouds to objectively identify surface damage, defects, cracks, and other anomalies based solely on geometric surface descriptors that are irrespective of point clouds’ underlying geometry. Non-temporal, in this case, refers to a single dataset, which is not relying on a change detection approach. The developed method utilizes vertex normal, surface variation, and curvature as three distinct surface descriptors to locate the likely damaged areas. Two synthetic datasets with planar and cylindrical geometries with known ground truth damage were created and used to test the developed workflow. In addition, the developed method was further validated on three real-world point cloud datasets using lidar and structure-from-motion techniques, which represented different underlying geometries and exhibited varying severity and mechanisms of damage. The analysis of the synthetic datasets demonstrated the robustness of the proposed damage detection method to classify vertices as surface damage with high recall and precision rates and a low false-positive rate. The real-world datasets illustrated the scalability of the damage detection method and its ability to classify areas as damaged and undamaged at the centimeter level. Moreover, the output classification of the damage detection method automatically bins the damaged vertices into different confidence intervals for further classification of detected likely damaged areas. Moving forward, the presented workflow can be used to bolster structural inspections by reducing subjectivity, enhancing reliability, and improving quantification in surface-evident damage.

scholarly journals Identification of shorelines for natural watercourses with the use of point cloud

2018 ◽  
Vol 71 ◽  
pp. 00014
Author(s):  
Ewa Sudoł
Keyword(s):  
Real Estate ◽  
Point Cloud ◽  
Point Clouds ◽  
The Other ◽  
Actual State ◽  
Water Law ◽  
Boundary Line ◽  
Technical Standards ◽  
Applicable Law ◽  
Vertical Sections

The article presents the method of identifying the shorelines for natural watercourses located in agricultural and forest areas in accordance with applicable law. In the process of developing methods for identification and verification of the actual course of watercourses, data from the cadastral map was used in the form of a vector drawing of borders and a database with border points in ZRD, BPP attributes, metadata and point clouds. The identification of the course of a watercourse on shrubbery and wooded areas as well as on-screen vectorization of the shoreline is cumbersome, and in some cases even impossible. In connection with the above, it has been proposed to use a point cloud and vertical sections prepared on their basis that run perpendicular to the edge of the watercourse. On their basis, the course of the shoreline was recognized in accordance with the definition contained in the Act on Water Law. Pursuant to § 9 para. 3a, beginning of the regulation that the land occupied by the natural seepage constitutes a separate cadastral plot within the boundary line, the suggested procedures for verifying the boundaries of watercourses can be used to update the land and building register databases. The identification of the boundaries of registered parcels made on the principles described in the publication may precede the activities of accepting the boundaries to the division of real estate. On the other hand, the course of the identified, in the mode of § 82a, the regulation the boundaries of registration plots constituting natural watercourses can be shown in the land and building register on the terms specified in art. 24 sec. 2b point 2) geodesy law, in order to replace data inconsistent with the actual state and applicable technical standards, respectively, data consistent with the actual state and applicable technical standards (§ 45 section 1 point 1 of the Regulation).

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