scholarly journals Imagery Network Fine Registration by Reference Point Cloud Data Based on the Tie Points and Planes

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 317
Author(s):  
Mehrdad Eslami ◽  
Mohammad Saadatseresht
Keyword(s):  
Point Cloud ◽  
Constraint Equation ◽  
Point Cloud Data ◽  
Random Errors ◽  
Object Space ◽  
Cloud Data ◽  
Feature Based ◽  
3D Information ◽  
Orientation Parameters ◽  
Fine Registration

Cameras and laser scanners are complementary tools for a 2D/3D information generation. Systematic and random errors cause the misalignment of the multi-sensor imagery and point cloud data. In this paper, a novel feature-based approach is proposed for imagery and point cloud fine registration. The tie points and its two neighbor pixels are matched in the overlap images, which are intersected in the object space to create the differential tie plane. A preprocessing is applied to the corresponding tie points and non-robust ones are removed. Initial coarse Exterior Orientation Parameters (EOPs), Interior Orientation Parameters (IOPs), and Additional Parameters (APs) are used to transform tie plane points to the object space. Then, the nearest points of the point cloud data to the transformed tie plane points are estimated. These estimated points are used to calculate Directional Vectors (DV) of the differential planes. As a constraint equation along with the collinearity equation, each object space tie point is forced to be located on the point cloud differential plane. Two different indoor and outdoor experimental data are used to assess the proposed approach. Achieved results show about 2.5 pixels errors on checkpoints. Such results demonstrated the robustness and practicality of the proposed approach.

scholarly journals A NOVEL TIE POINT BASED STRATEGY FOR POINT CLOUD AND IMAGERY DATA FINE REGISTRATION

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 331-334
Author(s):  
M. Eslami ◽  
M. Saadatseresht
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Normal Vector ◽  
Point Cloud Data ◽  
Object Space ◽  
Cloud Data ◽  
Conditional Equation ◽  
Novel Strategy ◽  
Orientation Parameters ◽  
Fine Registration

Abstract. Laser scanner generated point cloud and photogrammetric imagery are complimentary data for many applications and services. Misalignment between imagery and point cloud data is a common problem, which causes to inaccurate products and procedures. In this paper, a novel strategy is proposed for coarse to fine registration between close-range imagery and terrestrial laser scanner point cloud data. In such a case, tie points are extracted and matched from photogrammetric imagery and preprocessing is applied on generated tie points to eliminate non-robust ones. At that time, for every tie point, two neighbor pixels are selected and matched in all overlapped images. After that, coarse interior orientation parameters (IOPs) and exterior orientation parameters (EOPs) of the images are employed to reconstruct object space points of the tie point and its two neighbor pixels. Then, corresponding nearest points to the object space photogrammetric points are estimated in the point cloud data. Estimated three point cloud points are used to calculate a plane and its normal vector. Theoretically, every object space tie point should be located on the aforementioned plane, which is used as conditional equation alongside the collinearity equation to fine register the photogrammetric imagery network. Attained root mean square error (RMSE) results on check points, have been shown less than 2.3 pixels, which shows the accuracy, completeness and robustness of the proposed method.

Spatial feature-based prioritization for transmission of point cloud data in 3D-image sensor networks

2021 ◽  
pp. 1-1
Author(s):  
Masamichi Oka ◽  
Ryoichi Shinkuma ◽  
Takehiro Sato ◽  
Eiji Oki ◽  
Takanori Iwai ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Point Cloud ◽  
Image Sensor ◽  
Point Cloud Data ◽  
3D Image ◽  
Spatial Feature ◽  
Cloud Data ◽  
Feature Based

scholarly journals A Coarse-to-Fine Registration Approach for Point Cloud Data with Bipartite Graph Structure

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 263
Author(s):  
Munan Yuan ◽  
Xiru Li ◽  
Longle Cheng ◽  
Xiaofeng Li ◽  
Haibo Tan
Keyword(s):  
Bipartite Graph ◽  
Point Cloud ◽  
Graph Matching ◽  
Data Sets ◽  
Graph Structure ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Alignment Process ◽  
Coarse To Fine ◽  
Fine Registration

Alignment is a critical aspect of point cloud data (PCD) processing, and we propose a coarse-to-fine registration method based on bipartite graph matching in this paper. After data pre-processing, the registration progress can be detailed as follows: Firstly, a top-tail (TT) strategy is designed to normalize and estimate the scale factor of two given PCD sets, which can combine with the coarse alignment process flexibly. Secondly, we utilize the 3D scale-invariant feature transform (3D SIFT) method to extract point features and adopt fast point feature histograms (FPFH) to describe corresponding feature points simultaneously. Thirdly, we construct a similarity weight matrix of the source and target point data sets with bipartite graph structure. Moreover, the similarity weight threshold is used to reject some bipartite graph matching error-point pairs, which determines the dependencies of two data sets and completes the coarse alignment process. Finally, we introduce the trimmed iterative closest point (TrICP) algorithm to perform fine registration. A series of extensive experiments have been conducted to validate that, compared with other algorithms based on ICP and several representative coarse-to-fine alignment methods, the registration accuracy and efficiency of our method are more stable and robust in various scenes and are especially more applicable with scale factors.

A Feature-Based Section Curve Reconstructing Strategy

2013 ◽  
Vol 572 ◽  
pp. 155-158
Author(s):  
Hai Tao Zhu ◽  
Liang Cong
Keyword(s):  
Error Analysis ◽  
Point Cloud ◽  
Feature Recognition ◽  
Point Cloud Data ◽  
Curve Segment ◽  
Cloud Data ◽  
Modeling Process ◽  
Feature Based ◽  
Section Curve ◽  
2D To 3D

ntegrating section feature recognition with forward design is an effective method to reconstruct section curve and change feature architecture patterns from 2D to 3D. This paper proposes solutions to filter the points on the slices of point cloud data, automatically sequence the points on slices, recognize section curve feature, fit each curve segment and reconstruct section curves. All the relevant algorithms are implemented in Matlab. The point cloud data of sighting scope is used to validate the strategy. Also, Error analysis is carried out in Geomagic Studio. This strategy proves its feasibility and accuracy of completing reverse modeling process.

Scale and Pose-Invariant Feature Quality Inspection for Freeform Geometries in Additive Manufacturing

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Yu Jin ◽  
Harry Pierson ◽  
Haitao Liao
Keyword(s):  
Additive Manufacturing ◽  
Point Cloud ◽  
Quality Inspection ◽  
Point Cloud Data ◽  
Inspection Method ◽  
Cloud Data ◽  
Geometric Dimensioning And Tolerancing ◽  
Geometric Inspection ◽  
3D Scanners ◽  
Feature Based

Abstract Additive manufacturing (AM) has the unprecedented ability to create customized, complex, and nonparametric geometry, and it has made this ability accessible to individuals outside of traditional production environments. Geometric inspection technology, however, has yet to adapt to take full advantage of AM’s abilities. Coordinate measuring machines are accurate, but they are also slow, expensive to operate, and inaccessible to many AM users. On the other hand, 3D-scanners provide fast, high-density measurements, but there is a lack of feature-based analysis techniques for point cloud data. There exists a need for developing fast, feature-based geometric inspection techniques that can be implemented by users without specialized training in inspection according to geometric dimensioning and tolerancing conventions. This research proposes a new scale- and pose-invariant quality inspection method based on a novel location-orientation-shape (LOS) distribution derived from point cloud data. The key technique of the new method is to describe the shape and pose of key features via kernel density estimation and detect nonconformities based on statistical divergence. Numerical examples are provided and tests on physical AM builds are conducted to validate the method. The results show that the proposed inspection scheme is able to identify form, position, and orientation defects. The results also demonstrate how datum features can be incorporated into point cloud inspection, that datum features can be complex, nonparametric surfaces, and how the specification of datums can be more intuitive and meaningful, particularly for users without special training.

scholarly journals RESEARCH ON THE DIGITAL RETENTION MECHANISM OF TIBETAN BUDDHISM ARCHITECTURE BASED ON UAV AND TLS: A CASE STUDY OF BAOGUANG HALL

2021 ◽  
Vol VIII-M-1-2021 ◽  
pp. 95-100
Author(s):  
Z. Li ◽  
M. Hou ◽  
Y. Dong ◽  
J. Wang ◽  
Y. Ji ◽  
...  
Keyword(s):  
Point Cloud ◽  
Cloud Model ◽  
Laser Scanner ◽  
Retention Mechanism ◽  
Point Cloud Data ◽  
Architectural Heritage ◽  
Registration Method ◽  
Cloud Data ◽  
3D Information ◽  
Unique Style

Abstract. Tibetan Buddhist architecture embodies ancient Chinese architectural culture and religious culture. In the past, the information retention mechanisms for ancient buildings were implemented as photos, tracings, and rubbings, which cannot fundamentally document the authenticity of architectural heritage. To explore the digital retention method for the unique style of Han Tibetan architecture，this research that based on the idea of reverse documentation first collects point cloud data with the technical support of unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanner (TLS) and then uses registration method to obtain the integral of the point cloud model of Baoguang Hall. This paper explores the possibility of extracting 2D and 3D information, such as architectural plans, facades, decorative components, and models of the temple architecture, by processing point cloud data. Finally, this study proves the feasibility of using digital technology for the preservation and protection of architectural heritage.

scholarly journals Integration of Constructive Solid Geometry and Boundary Representation (CSG-BRep) for 3D Modeling of Underground Cable Wells from Point Clouds

2020 ◽  
Vol 12 (9) ◽  
pp. 1452
Author(s):  
Ming Huang ◽  
Xueyu Wu ◽  
Xianglei Liu ◽  
Tianhang Meng ◽  
Peiyuan Zhu
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Adaptive Method ◽  
Boundary Representation ◽  
Point Cloud Data ◽  
Terrestrial Lidar ◽  
Cloud Data ◽  
Underground Cable ◽  
3D Information

The preference of three-dimensional representation of underground cable wells from two-dimensional symbols is a developing trend, and three-dimensional (3D) point cloud data is widely used due to its high precision. In this study, we utilize the characteristics of 3D terrestrial lidar point cloud data to build a CSG-BRep 3D model of underground cable wells, whose spatial topological relationship is fully considered. In order to simplify the modeling process, first, point cloud simplification is performed; then, the point cloud main axis is extracted by OBB bounding box, and lastly the point cloud orientation correction is realized by quaternion rotation. Furthermore, employing the adaptive method, the top point cloud is extracted, and it is projected for boundary extraction. Thereupon, utilizing the boundary information, we design the 3D cable well model. Finally, the cable well component model is generated by scanning the original point cloud. The experiments demonstrate that, along with the algorithm being fast, the proposed model is effective at displaying the 3D information of the actual cable wells and meets the current production demands.

scholarly journals Microscopic 3D reconstruction based on point cloud data generated using defocused images

2021 ◽  
Vol 54 (9-10) ◽  
pp. 1309-1318
Author(s):  
Xiangjun Liu ◽  
Wenfeng Zheng ◽  
Yuanyuan Mou ◽  
Yulin Li ◽  
Lirong Yin
Keyword(s):  
3D Reconstruction ◽  
Real Time ◽  
Affine Transformation ◽  
Point Cloud ◽  
Information Needs ◽  
Image Sequence ◽  
Point Cloud Data ◽  
Reconstruction Process ◽  
Cloud Data ◽  
3D Information

Most of the 3D reconstruction requirements of microscopic scenes exist in industrial detection, and this scene requires real-time object reconstruction and can get object surface information quickly. However, this demand is challenging to obtain for micro scenarios. The reason is that the microscope’s depth of field is shallow, and it is easy to blur the image because the object’s surface is not in the focus plane. Under the video microscope, the images taken frame by frame are mostly defocused images. In the process of 3D reconstruction, a single sheet or a few 2D images are used for geometric-optical calculation, and the affine transformation is used to obtain the 3D information of the object and complete the 3D reconstruction. The feature of defocus image is that its complete information needs to be restored by a whole set of single view defocus image sequences. The defocused image cannot complete the task of affine transformation due to the lack of information. Therefore, using defocus image sequence to restore 3D information has higher processing difficulty than ordinary scenes, and the real-time performance is more difficult to guarantee. In this paper, the surface reconstruction process based on point-cloud data is studied. A Delaunay triangulation method based on plane projection and synthesis algorithm is used to complete surface fitting. Finally, the 3D reconstruction experiment of the collected image sequence is completed. The experimental results show that the reconstructed surface conforms to the surface contour information of the selected object.

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