scholarly journals 3D recognition based on ordered images reconstruction

2018 ◽  
Vol 232 ◽  
pp. 02045
Author(s):  
Ning Zhang ◽  
YongJia Zhao
Keyword(s):  
Point Cloud ◽  
Cloud Model ◽  
Scene Recognition ◽  
Reconstruction Method ◽  
Processing Stage ◽  
Local Descriptor ◽  
Image Frame ◽  
Online Processing ◽  
Offline Processing ◽  
Sequential Images

Nowadays, more and more applications require precise and quickly 3D recognition, such as augmented reality and robot navigation. In recent years, model-based methods can get accurate object or scene recognition, but it takes a lot of time to reconstruct the model. Therefore, we propose a fast 3D reconstruction method based on ordered images for robust and accurate 3D recognition. The proposed algorithm consists of two parts, the offline processing stage, and the online processing stage. First, in the offline processing stage, the sparse point cloud model of the scene or object is reconstructed based on the sequential images, optimized using the BA algorithm based on the local correlation frame, and then the local descriptor of the resulting model points is stored. Secondly, in the online processing stage, for each image frame of the camera video, a matching relationship between the stored point cloud and the 2D feature points on the image frame is established, based on which the pose of the camera can be solved accurately.

scholarly journals Dense Robust 3D Reconstruction and Measurement for 3D Printing Process Based on Vision

2021 ◽  
Vol 11 (17) ◽  
pp. 7961
Author(s):  
Ning Lv ◽  
Chengyu Wang ◽  
Yujing Qiao ◽  
Yongde Zhang
Keyword(s):  
3D Printing ◽  
3D Reconstruction ◽  
Point Cloud ◽  
Cloud Model ◽  
Texture Mapping ◽  
Open Loop ◽  
Reconstruction Method ◽  
Sift Algorithm ◽  
Printing Process ◽  
Process Detection

The 3D printing process lacks real-time inspection, which is still an open-loop manufacturing process, and the molding accuracy is low. Based on the 3D reconstruction theory of machine vision, in order to meet the applicability requirements of 3D printing process detection, a matching fusion method is proposed. The fast nearest neighbor (FNN) method is used to search matching point pairs. The matching point information of FFT-SIFT algorithm based on fast Fourier transform is superimposed with the matching point information of AKAZE algorithm, and then fused to obtain more dense feature point matching information and rich edge feature information. Combining incremental SFM algorithm with global SFM algorithm, an integrated SFM sparse point cloud reconstruction method is developed. The dense point cloud is reconstructed by PMVs algorithm, the point cloud model is meshed by Delaunay triangulation, and then the accurate 3D reconstruction model is obtained by texture mapping. The experimental results show that compared with the classical SIFT algorithm, the speed of feature extraction is increased by 25.0%, the number of feature matching is increased by 72%, and the relative error of 3D reconstruction results is about 0.014%, which is close to the theoretical error.

scholarly journals Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3493
Author(s):  
Gahyeon Lim ◽  
Nakju Doh
Keyword(s):  
Point Cloud ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Reconstruction Method ◽  
Complex Environments ◽  
Indoor Space ◽  
Multi Level ◽  
Level Building ◽  
Indoor Spaces ◽  
Data Term

Remarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spaces, which are constructed by room segmentation or horizontal slicing approach that divide the multi-room or multi-level building environments into several segments. In this study, we propose an automatic reconstruction method of multi-level indoor spaces with unique models, including inter-room and inter-floor connections from point cloud and trajectory. We construct structural points from registered point cloud and extract piece-wise planar segments from the structural points. Then, a three-dimensional space decomposition is conducted and water-tight meshes are generated with energy minimization using graph cut algorithm. The data term of the energy function is expressed as a difference in visibility between each decomposed space and trajectory. The proposed method allows modeling of indoor spaces in complex environments, such as multi-room, room-less, and multi-level buildings. The performance of the proposed approach is evaluated for seven indoor space datasets.

Simplification algorithm of denture point cloud based on feature preserving

2021 ◽  
pp. 1-14
Author(s):  
Shigang Wang ◽  
Shuai Peng ◽  
Jiawen He
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Characteristic Point ◽  
Cloud Model ◽  
Average Distance ◽  
Normal Vector ◽  
Sampling Point ◽  
Characteristic Region ◽  
K Value ◽  
Feature Preserving

Due to the point cloud of oral scan denture has a large amount of data and redundant points. A point cloud simplification algorithm based on feature preserving is proposed to solve the problem that the feature preserving is incomplete when processing point cloud data and cavities occur in relatively flat regions. Firstly, the algorithm uses kd-tree to construct the point cloud spatial topological to search the k-Neighborhood of the sampling point. On the basis of that to calculate the curvature of each point, the angle between the normal vector, the distance from the point to the neighborhood centroid, as well as the standard deviation and the average distance from the point to the neighborhood on this basis, therefore, the detailed features of point cloud can be extracted by multi-feature extraction and threshold determination. For the non-characteristic region, the non-characteristic point cloud is spatially divided through Octree to obtain the K-value of K-means clustering algorithm and the initial clustering center point. The simplified results of non-characteristic regions are obtained after further subdivision. Finally, the extracted detail features and the reduced result of non-featured region will be merged to obtain the final simplification result. The experimental results show that the algorithm can retain the characteristic information of point cloud model better, and effectively avoid the phenomenon of holes in the simplification process. The simplified results have better smoothness, simplicity and precision, and are of high practical value.

Structural mapping of rock walls imaged with a LiDAR mounted on an unmanned aircraft system

2019 ◽  
Vol 7 (1) ◽  
pp. 21-38 ◽  
Author(s):  
Connor McAnuff ◽  
Claire Samson ◽  
Dave Melanson ◽  
Christopher Polowick ◽  
Erin Bethell
Keyword(s):  
Point Cloud ◽  
Ground Level ◽  
Unmanned Aircraft ◽  
Reconstruction Method ◽  
Angle Of Incidence ◽  
Fracture Density ◽  
Mining Operations ◽  
Structural Mapping ◽  
Geological Information ◽  
Aircraft System

Structural mapping of rock walls to determine fracture orientation provides critical geological information in support of mining operations. A helicopter-style UAS (rotor diameter 2 m; take-off mass 35 kg; payload mass 11 kg) instrumented with a high-resolution LiDAR imaged a 75 m long and 10–15 m high series of four adjacent rock walls at the Canadian Wollastonite mine. A point cloud with a density of 484 point/m2 acquired at an angle of incidence of ∼41.7° from a flight altitude of 41.7 m above ground level was selected for structural mapping. The point cloud was first meshed using the Poisson surface reconstruction method and then remeshed to achieve an even element size distribution. Visualization of the remeshed Poisson mesh using a 360° hue–saturation–lightness colour wheel highlighted areas of higher fracture density, whereas visualization using a 180° colour wheel accentuated sliver-like geological features. Two joint sets were identified at 156/82 and 241/86 (strike/dip in degrees). A total of 18 virtual strike measurements and 13 virtual dip measurements were within 10% of manual compass measurements. This study demonstrated that the task of structural mapping of large rock walls can be automated by processing 3D images acquired with a LiDAR mounted on a UAS.

Automatic reconstruction method for large scene based on multi-site point cloud stitching

Measurement ◽  
2019 ◽  
Vol 131 ◽  
pp. 590-596 ◽  
Author(s):  
Haonan Xu ◽  
Lei Yu ◽  
Junyi Hou ◽  
Shumin Fei
Keyword(s):  
Point Cloud ◽  
Reconstruction Method
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