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 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.

A Model-Based Layer-to-Layer Control Algorithm for Ink-Jet 3D Printing

2014 ◽  
Author(s):  
Lu Lu ◽  
Jian Zheng ◽  
Sandipan Mishra
Keyword(s):  
3D Printing ◽  
Control Algorithm ◽  
Fixed Number ◽  
Open Loop ◽  
Droplet Shape ◽  
Printing Process ◽  
Model Based ◽  
Ink Jet ◽  
Profile Height ◽  
Layer Control

Ink-jet 3D printing is a promising technology for additive manufacturing, with the potential for impacting a wide variety of industries. In traditional ink-jet 3D printing, the part is built up by depositing droplets layer upon layer in an open-loop manner. Droplet and edge dimensions are typically predicted experimentally and are assumed to remain constant through the printing process. However, there is no guarantee of consistent droplet shape and dimensions or the smoothness of the finished parts due to uncertainties in the manufacturing process. To address this issue, we propose a model-based feedback control law for ink-jet 3D printing that uses a height sensor for measuring profile height after each layer for determining the appropriate layer patterns for subsequent layers. Towards this goal, a simple model describing the relationship between profile height change and droplet deposition in the layer building process is first proposed and experimentally identified. Based on this model, a closed-loop layer-to-layer control algorithm is then developed for the ink-jet printing process. Specifically, the proposed algorithm uses a model prediction control algorithm to minimize the difference between the predicted height and the desired height and the predicted surface unevenness after a fixed number of layers. Experimental results show that the algorithm is able to achieve more consistent shapes between layers, reduced edge shrinking of the part, and smoother surface of the top layer.

A Method of Point Cloud Stitching Based on the Mechanical Arm and Laser

2014 ◽  
Vol 988 ◽  
pp. 467-470
Author(s):  
Liang Liu ◽  
Shu Guang Dai
Keyword(s):  
3D Printing ◽  
3D Reconstruction ◽  
Coordinate System ◽  
Point Cloud ◽  
Three Dimensional ◽  
Good Effect ◽  
Point Cloud Data ◽  
Cloud Data

3D reconstruction as the basis of many applications,such as 3D printing, has become more and more importantfor many enterprises and researchersThe very important step in 3D reconstruction is the joining together of point cloud.This paper introduces the structures of a system to obtain three-dimensional point cloud data and a kind ofmethodsusing of the system to get point cloud data through the rotation and translation of the coordinate system, joining together the point cloud data.Experiment shows that this method has achieved good effect.

scholarly journals Fast and Robust 3D Reconstruction Solution from Permissive Open-Source Code

2021 ◽  
Vol 12 (1) ◽  
pp. 206-218
Author(s):  
Victor Gouveia de M. Lyra ◽  
Adam H. M. Pinto ◽  
Gustavo C. R. Lima ◽  
João Paulo Lima ◽  
Veronica Teichrieb ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Virtual Environments ◽  
Structure From Motion ◽  
Point Cloud ◽  
State Of The Art ◽  
Source Code ◽  
Texture Mapping ◽  
Large Datasets ◽  
Stereo Reconstruction ◽  
Object Models

With the growth of access to faster computers and more powerful cameras, the 3D reconstruction of objects has become one of the public's main topics of research and demand. This task is vigorously applied in creating virtual environments, creating object models, and other activities. One of the techniques for obtaining 3D features is photogrammetry, mapping objects and scenarios using only images. However, this process is very costly and can be pretty time-consuming for large datasets. This paper proposes a robust, efficient reconstruction pipeline with a low runtime in batch processing and permissive code. It is even possible to commercialize it without the need to keep the code open. We mix an improved structure from motion algorithm and a recurrent multi-view stereo reconstruction. We also use the Point Cloud Library for normal estimation, surface reconstruction, and texture mapping. We compare our results with state-of-the-art techniques using benchmarks and our datasets. The results showed a decrease of 69.4% in the average execution time, with high quality but a greater need for more images to achieve complete reconstruction.

scholarly journals AN AUTOMATIC 3D RECONSTRUCTION METHOD BASED ON MULTI-VIEW STEREO VISION FOR THE MOGAO GROTTOES

2015 ◽  
Vol XL-4/W5 ◽  
pp. 171-176 ◽  
Author(s):  
J. Xiong ◽  
S. Zhong ◽  
L. Zheng
Keyword(s):  
3D Reconstruction ◽  
Stereo Vision ◽  
Measurement System ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Texture Mapping ◽  
Reconstruction Method ◽  
Epipolar Constraint ◽  
Mogao Grottoes ◽  
Binocular Stereo

This paper presents an automatic three-dimensional reconstruction method based on multi-view stereo vision for the Mogao Grottoes. 3D digitization technique has been used in cultural heritage conservation and replication over the past decade, especially the methods based on binocular stereo vision. However, mismatched points are inevitable in traditional binocular stereo matching due to repeatable or similar features of binocular images. In order to reduce the probability of mismatching greatly and improve the measure precision, a portable four-camera photographic measurement system is used for 3D modelling of a scene. Four cameras of the measurement system form six binocular systems with baselines of different lengths to add extra matching constraints and offer multiple measurements. Matching error based on epipolar constraint is introduced to remove the mismatched points. Finally, an accurate point cloud can be generated by multi-images matching and sub-pixel interpolation. Delaunay triangulation and texture mapping are performed to obtain the 3D model of a scene. The method has been tested on 3D reconstruction several scenes of the Mogao Grottoes and good results verify the effectiveness of the method.

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