scholarly journals Three-Dimensional Reconstruction Method of Rapeseed Plants in the Whole Growth Period Using RGB-D Camera

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4628
Author(s):  
Xiaowen Teng ◽  
Guangsheng Zhou ◽  
Yuxuan Wu ◽  
Chenglong Huang ◽  
Wanjing Dong ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Depth Image ◽  
Scanning System ◽  
Reconstruction Method ◽  
Three Dimensional Reconstruction ◽  
Hardware Cost ◽  
Dimensional Reconstruction

The three-dimensional reconstruction method using RGB-D camera has a good balance in hardware cost and point cloud quality. However, due to the limitation of inherent structure and imaging principle, the acquired point cloud has problems such as a lot of noise and difficult registration. This paper proposes a 3D reconstruction method using Azure Kinect to solve these inherent problems. Shoot color images, depth images and near-infrared images of the target from six perspectives by Azure Kinect sensor with black background. Multiply the binarization result of the 8-bit infrared image with the RGB-D image alignment result provided by Microsoft corporation, which can remove ghosting and most of the background noise. A neighborhood extreme filtering method is proposed to filter out the abrupt points in the depth image, by which the floating noise point and most of the outlier noise will be removed before generating the point cloud, and then using the pass-through filter eliminate rest of the outlier noise. An improved method based on the classic iterative closest point (ICP) algorithm is presented to merge multiple-views point clouds. By continuously reducing both the size of the down-sampling grid and the distance threshold between the corresponding points, the point clouds of each view are continuously registered three times, until get the integral color point cloud. Many experiments on rapeseed plants show that the success rate of cloud registration is 92.5% and the point cloud accuracy obtained by this method is 0.789 mm, the time consuming of a integral scanning is 302 seconds, and with a good color restoration. Compared with a laser scanner, the proposed method has considerable reconstruction accuracy and a significantly ahead of the reconstruction speed, but the hardware cost is much lower when building a automatic scanning system. This research shows a low-cost, high-precision 3D reconstruction technology, which has the potential to be widely used for non-destructive measurement of rapeseed and other crops phenotype.

scholarly journals Research on Outdoor Garden Scene Reconstruction Based on PMVS Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wei He
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Feature Matching ◽  
Rapid Development ◽  
Three Dimensional ◽  
Reconstruction Method ◽  
Feature Points ◽  
Three Dimensional Reconstruction ◽  
Dimensional Reconstruction ◽  
Internet Of Things Technology

The three-dimensional reconstruction of outdoor landscape is of great significance for the construction of digital city. With the rapid development of big data and Internet of things technology, when using the traditional image-based 3D reconstruction method to restore the 3D information of objects in the image, there will be a large number of redundant points in the point cloud and the density of the point cloud is insufficient. Based on the analysis of the existing three-dimensional reconstruction technology, combined with the characteristics of outdoor garden scene, this paper gives the detection and extraction methods of relevant feature points and adopts feature matching and repairing the holes generated by point cloud meshing. By adopting the candidate strategy of feature points and adding the mesh subdivision processing method, an improved PMVS algorithm is proposed and the problem of sparse point cloud in 3D reconstruction is solved. Experimental results show that the proposed method not only effectively realizes the three-dimensional reconstruction of outdoor garden scene, but also improves the execution efficiency of the algorithm on the premise of ensuring the reconstruction effect.

scholarly journals 3D Reconstruction Method of Rapeseed Plants in the Whole Growth Period Using RGB-D Camera

2021 ◽  
Author(s):  
Xiaowen Teng ◽  
Guangsheng Zhou ◽  
Yuxuan Wu ◽  
Chenglong Huang ◽  
Wanjing Dong ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Near Infrared ◽  
Infrared Image ◽  
Depth Map ◽  
Point Clouds ◽  
Scanning System ◽  
Reconstruction Method ◽  
Hardware Cost ◽  
Scan Time

The 3D reconstruction method using RGB-D camera has a good balance in hardware cost, point cloud quality and automation. However, due to the limitation of inherent structure and imaging principle, the acquired point cloud has problems such as a lot of noise and difficult registration. This paper proposes a three-dimensional reconstruction method using Azure Kinect to solve these inherent problems. Shoot color map, depth map and near-infrared image of the target from six perspectives by Azure Kinect sensor. Multiply the 8-bit infrared image binarization with the general RGB-D image alignment result provided by Microsoft to remove ghost images and most of the background noise. In order to filter the floating point and outlier noise of the point cloud, a neighborhood maximum filtering method is proposed to filter out the abrupt points in the depth map. The floating points in the point cloud are removed before generating the point cloud, and then using the through filter filters out outlier noise. Aiming at the shortcomings of the classic ICP algorithm, an improved method is proposed. By continuously reducing the size of the down-sampling grid and the distance threshold between the corresponding points, the point clouds of each view are continuously registered three times, until get the complete color point cloud. A large number of experimental results on rape plants show that the point cloud accuracy obtained by this method is 0.739mm, a complete scan time is 338.4 seconds, and the color reduction is high. Compared with a laser scanner, the proposed method has considerable reconstruction accuracy and a significantly ahead of the reconstruction speed, but the hardware cost is much lower and it is easy to automate the scanning system. This research shows a low-cost, high-precision 3D reconstruction technology, which has the potential to be widely used for non-destructive measurement of crop phenotype.

Three-dimensional reconstruction method of Tang Dynasty building based on point clouds

2015 ◽  
Vol 54 (12) ◽  
pp. 123111 ◽  
Author(s):  
Yinghui Wang ◽  
Huanhuan Zhang ◽  
Yanni Zhao ◽  
Wen Hao ◽  
Xiaojuan Ning ◽  
...  
Keyword(s):  
Tang Dynasty ◽  
Three Dimensional ◽  
Point Clouds ◽  
Reconstruction Method ◽  
Three Dimensional Reconstruction ◽  
Dimensional Reconstruction

Research on Three-Dimensional Reconstruction Technology Based on the Data of Hybrid Measurement

2014 ◽  
Vol 1039 ◽  
pp. 30-35
Author(s):  
Wei Liu ◽  
Lu Yue Ju ◽  
Cheng Hui Lin
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Parametric Modeling ◽  
Three Dimensional Reconstruction ◽  
Three Dimensional Model ◽  
Registration Method ◽  
Engine Part ◽  
Dimensional Reconstruction ◽  
Feature Based

Hybrid measurement method is proposed to solve the problem that the partial or whole three-dimensional reconstruction accuracy of aviation engine parts is high. The point clouds of the aviation engine part are captured first using contact and non-contact measuring method. Feature-based parametric modeling strategy is adopted to reconstruct the aviation engine part so that it is easy to be modified in the future. Then, the point cloud data obtained by contact measurement and the reconstructed model are registrated to the same coordinate system to detect the deviation. The point cloud registration method is based upon the feature-based registration method and standard Iterative Closest Point (ICP) algorithm, which help to improve the accuracy of registration. According to the result of deviation, the three-dimensional model can be modified. The accuracy of the modified model is controlled within 0.02mm, satisfying the requirement of aviation engine parts. Three-dimensional reconstruction results have verified the feasibility of the method.

REAL-TIME THREE-DIMENSIONAL RECONSTRUCTION FOR VOLUME-BASED SURGERY SIMULATIONS

2008 ◽  
Vol 20 (04) ◽  
pp. 205-218 ◽  
Author(s):  
Jyh-Fa Lee ◽  
Ming-Shium Hsieh ◽  
Chih-Wei Kuo ◽  
Ming-Dar Tsai ◽  
Ming Ma
Keyword(s):  
3D Reconstruction ◽  
Real Time ◽  
Three Dimensional ◽  
Reconstruction Method ◽  
Three Dimensional Reconstruction ◽  
Visual Responses ◽  
Haptic Interactions ◽  
Tissue Surfaces ◽  
Dimensional Reconstruction ◽  
Time Critical

This paper describes a three-dimensional reconstruction method to provide real-time visual responses for volume (constituted by tomographic slices) based surgery simulations. The proposed system uses dynamical data structures to record tissue triangles obtained from 3D reconstruction computation. Each tissue triangle in the structures can be modified or every structure can be deleted or allocated independently. Moreover, triangle reconstruction is optimized by only deleting or adding vertices from manipulated voxels that are classified as erosion (in which the voxels are changed from tissue to null) or generation (the voxels are changed from null to tissue). Therefore, by manipulating these structures, 3D reconstruction can be locally implemented for only manipulated voxels to achieve the highest efficiency without reconstructing tissue surfaces in the whole volume as general methods do. Three surgery simulation examples demonstrate that the proposed method can provide time-critical visual responses even under other time-consuming computations such as volume manipulations and haptic interactions.

scholarly journals Online Measurement of Deposit Surface in Electron Beam Freeform Fabrication

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4001 ◽  
Author(s):  
Shuhe Chang ◽  
Haoyu Zhang ◽  
Haiying Xu ◽  
Xinghua Sang ◽  
Li Wang ◽  
...  
Keyword(s):  
Electron Beam ◽  
3D Reconstruction ◽  
Three Dimensional ◽  
Thermal Conditions ◽  
Online Measurement ◽  
Three Dimensional Reconstruction ◽  
Continuous Change ◽  
Position Information ◽  
Freeform Fabrication ◽  
Dimensional Reconstruction

In the process of electron beam freeform fabrication (EBF3), due to the continuous change of thermal conditions and variability in wire feeding in the deposition process, geometric deviations are generated in the deposition of each layer. In order to prevent the layer-by-layer accumulation of the deviation, it is necessary to perform online geometry measurement for each deposition layer, based on which the error compensation can be done for the previous deposition layer in the next deposition layer. However, the traditional three-dimensional reconstruction method that employs structured laser cannot meet the requirements of long-term stable operation in the manufacturing process of EBF3. Therefore, this paper proposes a method to measure the deposit surfaces based on the position information of electron beam speckle, in which an electron beam is used to bombard the surface of the deposit to generate the speckle. Based on the structured information of the electron beam in the vacuum chamber, the three-dimensional reconstruction of the surface of the deposited parts is realized without need of additional structured laser sensor. In order to improve the detection accuracy, the detection error is theoretically analyzed and compensated. The absolute error after compensation is smaller than 0.1 mm, and the precision can reach 0.1%, which satisfies the requirements of 3D reconstruction of the deposited parts. An online measurement system is built for the surface of deposited parts in the process of electron beam freeform fabrication, which realizes the online 3D reconstruction of the surface of the deposited layer. In addition, in order to improve the detection stability of the whole system, the image processing algorithm suitable for this scene is designed. The reliability and speed of the algorithm are improved by ROI extraction, threshold segmentation, and expansion corrosion. In addition, the speckle size information can also reflect the thermal conditions of the surface of the deposited parts. Hence, it can be used for online detection of defects such as infusion and voids.

scholarly journals Diagnostic Method of Maxillomandibular Relation of Edentulous Ridges. Application of the Three-dimensional Reconstruction Method.

1995 ◽  
Vol 39 (6) ◽  
pp. 995-1003 ◽  
Author(s):  
Naotsugu Kawahata ◽  
Hideki Ono ◽  
Akihiko Otsuka ◽  
Tomomi Fukunaga ◽  
Yuji Kamashita ◽  
...  
Keyword(s):  
Diagnostic Method ◽  
Three Dimensional ◽  
Reconstruction Method ◽  
Three Dimensional Reconstruction ◽  
Dimensional Reconstruction

Kinect Depth Data Segmentation Based on Gauss Mixture Model Clustering

2013 ◽  
Vol 760-762 ◽  
pp. 1556-1561
Author(s):  
Ting Wei Du ◽  
Bo Liu
Keyword(s):  
Mixture Model ◽  
Point Cloud ◽  
Three Dimensional ◽  
Image Data ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Depth Image ◽  
Cloud Data ◽  
Mixture Model Clustering ◽  
Gauss Mixture

Indoor scene understanding based on the depth image data is a cutting-edge issue in the field of three-dimensional computer vision. Taking the layout characteristics of the indoor scenes and more plane features in these scenes into account, this paper presents a depth image segmentation method based on Gauss Mixture Model clustering. First, transform the Kinect depth image data into point cloud which is in the form of discrete three-dimensional point data, and denoise and down-sample the point cloud data; second, calculate the point normal of all points in the entire point cloud, then cluster the entire normal using Gaussian Mixture Model, and finally implement the entire point clouds segmentation by RANSAC algorithm. Experimental results show that the divided regions have obvious boundaries and segmentation quality is above normal, and lay a good foundation for object recognition.

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