A Efficient Surface Reconstruction Method for Noisy Samples Based on Bilateral Filtering and down Sampling

2014 ◽  
Vol 950 ◽  
pp. 145-149
Author(s):  
Wen Rui Wan
Keyword(s):  
Surface Reconstruction ◽  
Sampling Method ◽  
Reconstruction Algorithm ◽  
Bilateral Filter ◽  
Reconstruction Method ◽  
Bilateral Filtering ◽  
Triangle Mesh ◽  
Feature Size ◽  
Original Surface ◽  
Local Feature Size

Surface reconstruction is a hot topic in the field of computer graphics. Power Crust algorithm can reconstruct a triangle mesh that is topologically valid and convergent to the original surface. But it can not handle the points with noised and its running time is long. In this paper an efficient surface reconstruction algorithm for noisy samples is proposed. Firstly, we delete the noise by bilateral filter. Secondly, a non-uniformly sampling method is used to resample the input data in order decrease the number of the samples to the local feature size before reconstruction. Finally, Power crust algorithm is be used to reconstructed the surface. From the experiments, it can be seen the speed of reconstruction is increased and the features of the surface are preserved.

A New Surface Reconstruction Algorithm for Noisy 3D Unorganized Points

2012 ◽  
Vol 217-219 ◽  
pp. 1312-1317
Author(s):  
Jun Song
Keyword(s):  
Surface Reconstruction ◽  
Point Cloud ◽  
Sampling Method ◽  
Reconstruction Algorithm ◽  
Improved Method ◽  
Feature Size ◽  
Data Set ◽  
Good Surface ◽  
Fuzzy C Means Clustering ◽  
Local Feature Size

This paper puts forward a new method of surface reconstruction. Power crust algorithm can reconstruct a good surface that is topological valid and be proved theoretically. But when the point cloud is noisy, the surface reconstructed is not good and its running time is long. This paper proposes a improved method of fuzzy c-means clustering to delete the noisy points and a non-uniformly sampling method to resample the input data set according to the local feature size before reconstruction. Experimental results show that the efficiency of the algorithm has been improved much more.

scholarly journals Adaptive Metrics for Adaptive Samples

Algorithms ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 200
Author(s):  
Nicholas J. Cavanna ◽  
Donald R. Sheehy
Keyword(s):  
Euclidean Space ◽  
Surface Reconstruction ◽  
Critical Points ◽  
Adaptive Sampling ◽  
Local Feature ◽  
Distance Functions ◽  
Feature Size ◽  
Homology Inference ◽  
Definition Of ◽  
Local Feature Size

We generalize the local-feature size definition of adaptive sampling used in surface reconstruction to relate it to an alternative metric on Euclidean space. In the new metric, adaptive samples become uniform samples, making it simpler both to give adaptive sampling versions of homological inference results and to prove topological guarantees using the critical points theory of distance functions. This ultimately leads to an algorithm for homology inference from samples whose spacing depends on their distance to a discrete representation of the complement space.

Adaptive Subdivision Surface Reconstruction for Scattered Data in Reverse Engineering Based on GPU

2012 ◽  
Vol 490-495 ◽  
pp. 138-142
Author(s):  
Ying Hui Wang ◽  
Wei Yong Wu
Keyword(s):  
Reverse Engineering ◽  
Surface Reconstruction ◽  
Large Scale ◽  
High Efficiency ◽  
Reconstruction Algorithm ◽  
Scattered Data ◽  
Data Sets ◽  
Reconstruction Method ◽  
Subdivision Surface ◽  
Adaptive Subdivision

Reconstructing geometry models from scattered data is an important task in reverse engineering. An adaptive subdivision surface reconstruction method was proposed to construct complex models rapidly. This method includes several steps: triangulation on scattered data; mesh segmentation and simplification; computing the subdivision depth according to the specified error. The last step is computing mesh control net by fitting subdivision functions and construct subdivision surface adaptively. In order to improve the efficiency of the algorithm, we implemented the reconstruction algorithm on GPU in parallel way and tested the program on several large scale data sets. Our adaptive subdivision method can save storage space and gain high efficiency simultaneously.

scholarly journals A Lightweight Surface Reconstruction Method for Online 3D Scanning Point Cloud Data Oriented toward 3D Printing

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Buyun Sheng ◽  
Feiyu Zhao ◽  
Xiyan Yin ◽  
Chenglei Zhang ◽  
Hui Wang ◽  
...  
Keyword(s):  
3D Printing ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Reconstruction Algorithm ◽  
3D Scanning ◽  
Reconstruction Method ◽  
Point Cloud Data ◽  
Registration Method ◽  
Cloud Data ◽  
Web Environment

The existing surface reconstruction algorithms currently reconstruct large amounts of mesh data. Consequently, many of these algorithms cannot meet the efficiency requirements of real-time data transmission in a web environment. This paper proposes a lightweight surface reconstruction method for online 3D scanned point cloud data oriented toward 3D printing. The proposed online lightweight surface reconstruction algorithm is composed of a point cloud update algorithm (PCU), a rapid iterative closest point algorithm (RICP), and an improved Poisson surface reconstruction algorithm (IPSR). The generated lightweight point cloud data are pretreated using an updating and rapid registration method. The Poisson surface reconstruction is also accomplished by a pretreatment to recompute the point cloud normal vectors; this approach is based on a least squares method, and the postprocessing of the PDE patch generation was based on biharmonic-like fourth-order PDEs, which effectively reduces the amount of reconstructed mesh data and improves the efficiency of the algorithm. This method was verified using an online personalized customization system that was developed with WebGL and oriented toward 3D printing. The experimental results indicate that this method can generate a lightweight 3D scanning mesh rapidly and efficiently in a web environment.

Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

2020 ◽  
Vol 16 (2) ◽  
pp. 156-163
Author(s):  
Jingwen Wang ◽  
Xu Wang ◽  
Dan Yang ◽  
Kaiyang Wang
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Fourier Coefficients ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Measurement Equation ◽  
Image Reconstruction Method ◽  
Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

scholarly journals Performance Analysis of Surface Reconstruction Algorithms in Vertical Scanning Interferometry Based on Coherence Envelope Detection

Micromachines ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 164
Author(s):  
Dongxu Wu ◽  
Fusheng Liang ◽  
Chengwei Kang ◽  
Fengzhou Fang
Keyword(s):  
Surface Reconstruction ◽  
Measurement Accuracy ◽  
Experimental Studies ◽  
Reconstruction Algorithm ◽  
Low Noise ◽  
Surface Measurement ◽  
Reconstruction Algorithms ◽  
Centroid Method ◽  
Envelope Detection ◽  
Vertical Scanning Interferometry

Optical interferometry plays an important role in the topographical surface measurement and characterization in precision/ultra-precision manufacturing. An appropriate surface reconstruction algorithm is essential in obtaining accurate topography information from the digitized interferograms. However, the performance of a surface reconstruction algorithm in interferometric measurements is influenced by environmental disturbances and system noise. This paper presents a comparative analysis of three algorithms commonly used for coherence envelope detection in vertical scanning interferometry, including the centroid method, fast Fourier transform (FFT), and Hilbert transform (HT). Numerical analysis and experimental studies were carried out to evaluate the performance of different envelope detection algorithms in terms of measurement accuracy, speed, and noise resistance. Step height standards were measured using a developed interferometer and the step profiles were reconstructed by different algorithms. The results show that the centroid method has a higher measurement speed than the FFT and HT methods, but it can only provide acceptable measurement accuracy at a low noise level. The FFT and HT methods outperform the centroid method in terms of noise immunity and measurement accuracy. Even if the FFT and HT methods provide similar measurement accuracy, the HT method has a superior measurement speed compared to the FFT method.

Re-Sampled Based Mixed Surface Reconstruction Algorithm

2009 ◽  
Author(s):  
Rui Wang ◽  
Junshan Li ◽  
Guoqing Liu ◽  
Lingxia Liu
Keyword(s):  
Surface Reconstruction ◽  
Reconstruction Algorithm

Application Research on a Curved CAM Surface Reconstruction Method

2014 ◽  
Vol 971-973 ◽  
pp. 402-405
Author(s):  
Zhou Wen ◽  
Jun Ling Zhang ◽  
Xiu Duan Gong
Keyword(s):  
Reverse Engineering ◽  
Surface Reconstruction ◽  
Design Method ◽  
Subsequent Development ◽  
Reconstruction Method ◽  
Application Research ◽  
Cam Mechanism ◽  
Indexing Mechanism ◽  
Contour Surface ◽  
Rapid Modeling

Globular indexing CAM mechanism is a good indexing mechanism. As the working curve of CAM contour surface is no extending curved surface, there is certain difficulty to design processing. It is new kinds of design method that reverse engineering apply in rapid modeling of curved CAM. In this way, designer can complete curve of CAM reverse modeling, and the rationality of the model is verified. At the same time, it also can reverse modeling and the subsequent development of other products to provide a reference.

Reconstruction of Virtual Parts from Unorganized Scanned Data for Automated Dimensional Inspection

2003 ◽  
Vol 3 (1) ◽  
pp. 76-86 ◽  
Author(s):  
Chuan-Chu Kuo ◽  
Hong-Tzong Yau
Keyword(s):  
Surface Reconstruction ◽  
Delaunay Triangulation ◽  
Reconstruction Method ◽  
Geometric Complexity ◽  
Dimensional Inspection ◽  
Triangulated Surface ◽  
Surface Models ◽  
Sample Data ◽  
The Creation

In the framework of Virtual CMM [1], virtual parts are proposed to be constructed as triangulated surface models. This paper presents a novel surface reconstruction method to the creation of virtual parts. It is based on the idea of identification and sculpting of concave regions of a Delaunay triangulation of the sample data. The proposed algorithm is capable of handling the reconstruction of surfaces with or without boundaries from unorganized points. Comparisons with other Delaunay-based algorithms show that it is more efficient in that it can optimally adapt to the geometric complexity of the sampled object. To validate the proposed algorithm, some detailed illustrations are given.

scholarly journals Surface reconstruction amendment to the intrinsic sampling method

2018 ◽  
Vol 149 (23) ◽  
pp. 234705
Author(s):  
Francis G. J. Longford ◽  
Jonathan W. Essex ◽  
Chris-Kriton Skylaris ◽  
Jeremy G. Frey
Keyword(s):  
Surface Reconstruction ◽  
Sampling Method
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