Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2018 (2018), Article ID 7090186, 9 pages
https://doi.org/10.1155/2018/7090186
Research Article

Efficient 3D Volume Reconstruction from a Point Cloud Using a Phase-Field Method

1Department of Mathematics, Korea University, Seoul 02841, Republic of Korea
2School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China
3ROKIT Inc., Seoul 08512, Republic of Korea

Correspondence should be addressed to Junseok Kim

Received 22 November 2017; Accepted 1 January 2018; Published 6 February 2018

Academic Editor: Costică Moroșanu

Copyright © 2018 Darae Jeong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We propose an explicit hybrid numerical method for the efficient 3D volume reconstruction from unorganized point clouds using a phase-field method. The proposed three-dimensional volume reconstruction algorithm is based on the 3D binary image segmentation method. First, we define a narrow band domain embedding the unorganized point cloud and an edge indicating function. Second, we define a good initial phase-field function which speeds up the computation significantly. Third, we use a recently developed explicit hybrid numerical method for solving the three-dimensional image segmentation model to obtain efficient volume reconstruction from point cloud data. In order to demonstrate the practical applicability of the proposed method, we perform various numerical experiments.

1. Introduction

In this paper, we propose an efficient and robust algorithm for volume reconstruction from a point cloud. Reconstructing the three-dimensional model from a point cloud is important in medical applications. Surface reconstruction from a point cloud is a process of finding a surface model that approximates an unknown surface for a given set of sample points lying on or near the unknown surface [1].

Hoppe et al. developed an algorithm to reconstruct a surface in the three-dimensional space from unorganized points scattered on or near the unknown surface. The algorithm is based on the idea of determining the zero level set of a signed distance function [2]. Kazhdan proposed a surface reconstruction method which takes an oriented point set and returns a solid model. The method uses Stokes’ theorem to calculate the characteristic function (one inside the model and zero outside of it) of the solid model [3]. To reconstruct implicit surfaces from scattered unorganized data set, Li et al. presented a novel numerical method for surface embedding narrow volume reconstruction from unorganized points [4, 5]. Yang et al. proposed a 3D reconstruction technique from nonuniform point clouds via local hierarchical clustering [6]. Zhao et al. developed a fast sweeping level set and tagging methods [7]. Yezzi Jr. et al. proposed a new medical image segmentation based on feature-based metrics on a given image [8].

Beneš et al. used the Allen-Cahn equation with a forcing term to achieve image segmentation [9]. Caselles et al. proposed a model for active contours which could extract smooth shapes and could be adapted to find several contours simultaneously [10]. Methods using geometric active contour were introduced in [1114]. Zhang et al. developed a weighted sparse penalty and a weighted grouping effect penalty in modeling the subspace structure [15]. Chen used an ICKFCM method (ICA analysis and KFCM algorithm) in medical image segmentation and made a good result in extracting the complicated images [16]. Zhang et al. proposed a novel fuzzy level set method based on finding the minimum of energy function to locate the true object boundaries effectively [17]. Other numerical studies based on level set method were also introduced in [18, 19].

In this article, we propose an explicit hybrid algorithm for volume reconstruction from a point cloud. Therefore, it does not need implicit solvers such as multigrid methods. The computation is fast and efficient because the proposed algorithm uses a narrow band domain and a good initial condition.

This paper is organized as follows. In Section 2, we describe a mathematical model and a numerical solution algorithm for volume reconstruction from a point cloud. We present the numerical results for several examples in Section 3. In Section 4, we conclude.

2. Mathematical Model and Numerical Solution Algorithm

Now, we propose an explicit hybrid numerical method for volume reconstruction from a point cloud using a phase-field method. For in the two-dimensional space or in the three-dimensional space, denote the point cloud in the two- or the three-dimensional space, respectively. The geometric active contour model based on the mean curvature motion is given by the following evolution equation [20]:where is an edge indicator function, , and is a constant which is related to the phase transition width. Note that here we use a different edge indicator function and efficient explicit numerical algorithm.

For simplicity of exposition, we first discretize (1) in the two-dimensional space . Let be the uniform mesh size, where and are the number of grid points. Let be the discrete domain. Let be approximations of , where is the time step. Let be the distance to the data , where . In fact, we will use the distance function as an edge indicator function, . In practice, for , we define a local domain which embeds the point and set the minimum value at the grid point between the point and the grid point. For example, is a grid. Then, the computational narrow band domain is defined asOutside the narrow band domain, we set a large value to the edge indicator function, see Figure 1 for the procedure.

Figure 1: Schematic of defined distance function. (a) Point cloud data, (b) mesh grid covering the point cloud and local mesh grid, (c) local mesh grid embedding a point , (d) narrow band domain, and (e) edge indicator function, .

In this study, we apply the simplest sequential splitting procedure. We split (1) into two equations by using the operator splitting method:For a good initial configuration, we set inside on the narrow band domain and set outside the narrow band domain.

Given , we solve (3) on the narrow band domain by using the explicit Euler method:where is the intermediate value which is defined at point . The initial values at inside and outside region of the narrow band domain act as Dirichlet boundary condition in computing the discrete Laplace operator, .

Then, we analytically solve (4) by the method of separation of variables [21, 22]. That is, by analytically solvingThe analytic solution is given asTherefore, (5) and (7) consist of an efficient and robust algorithm for volume reconstruction from a point cloud. We should note that the proposed numerical solution algorithm is fully explicit. Therefore, we do not need an iterative method such as multigrid method to solve the governing equation. Also, the implementation of the algorithm is straightforward.

3. Computational Experiments

3.1. Two-Dimensional Experiments
3.1.1. Motion by Mean Curvature

To test the proposed numerical scheme, we perform a numerical experiment. The test is motion by mean curvature. If we set and , then the governing equation (1) becomes the original Allen-Cahn equation [23], which is a reaction-diffusion equation describing the process of phase separation in a binary alloy mixture. In the two-dimensional case, as approaches zero, the zero level set of evolves with the following velocity:where is the normal velocity, is the curvature, and is the radius of curvature at the point of the zero level set [24]. Then, (8) is rewritten by with . Therefore, analytic solution is given as .

On the computational domain , we investigate the motion by mean curvature of the circle in the annulus narrow band domain: as shown in Figure 2(a). We define an initial condition asIn this numerical simulation, we use the following parameters: , , , , and . Figures 2(a) and 2(b) show the temporal evolution of the initial circle and its radius with respect to time, respectively. For verification of our numerical results, we include the results of the analytic solution. As shown in Figure 2, the initial circle shrinks under the motion by mean curvature.

Figure 2: Temporal evolutions of the radius with up to in the two-dimensional space. (a) Zero level contour and (b) radius of circle with respect to time.
3.1.2. The Basic Working Mechanism of the Algorithm

The edge indicator function is close to zero where the point cloud exists. Therefore, the evolution will stop or slow down in the neighborhood of the point cloud. In (1), makes the phase-field shrink until it reaches the point cloud by the mean curvature flow. If the geometry of the point cloud is not convex, then the term makes the level set of the phase-field further shrink. For more details, please refer to [20]. To confirm the working mechanism of the algorithm, the temporal evolution of the interface in the two-dimensional space is shown in Figure 3. Here, we use the following parameters: , , and .

Figure 3: Temporal evolution of the interface: (a) initial condition, (b) 100 iterations, and (c) 1000 iterations.
3.2. Three-Dimensional Experiments

Next, we discretize (1) in the three-dimensional space, that is, . Let be the uniform mesh size, where , , and are the total number of grid points. Let be the discrete domain. We define as approximations of , where is the time step size. Let be the distance to the data , where .

Figure 4 represents construction of the three-dimensional distance function and the narrow band domain. In Figures 4(a) and 4(b), we can see the given point cloud data on computational grid and a local mesh of single point . Here, we calculate the distance between the given point and the grid points on the local mesh . The distance function is defined by the shortest one among the distance. Then, we obtain the narrow band domain which is determined by the distance function.

Figure 4: Construction of the three-dimensional distance function and the narrow band domain: (a) point cloud data and mesh, (b) cross section of point cloud and a local mesh of single point , (c) local mesh grid embedding a point , and (d) narrow band domain which is determined by the distance function.

Now, we can straightforwardly extend the two-dimensional numerical solutions (5) and (7) to the following three-dimensional solutions:

3.2.1. Reconstruction from Various Point Clouds

First, we reconstruct volume of Happy Buddha from the given scattered points () as shown in Figure 5(a) [25]. For numerical test, we use the following parameters: , , , , , , , and . In the first and second rows in Figure 5, we can see the front and back views of Happy Buddha. By the proposed scheme, we obtain the numerical solution after 200 iterations (see Figure 5(c)) with the initial condition in Figure 5(b).

Figure 5: Front and back views of Happy Buddha: (a) point clouds, (b) initial condition of , and (c) numerical solution after 200 iterations.

Next, we reconstruct volume of Armadillo model from the given scattered points () as shown in Figure 6(a) [25]. For numerical test, we use the following parameters: , , , , , , , and . In the first and second rows in Figure 6, we can see the front and back views of Armadillo model. By the proposed scheme, we obtain the numerical solution after 400 iterations (see Figure 6(c)) with the initial condition in Figure 6(b).

Figure 6: Front and back views of Armadillo model: (a) initial point clouds, (b) initial condition of , and (c) numerical solution after 400 iterations.

As the final example, we reconstruct volume of Stanford Dragon from the given scattered points () as shown in Figure 7(a) [25]. For numerical test, we use the following parameters: , , , , , , , and . In the first and second rows in Figure 7, we can see the front and back views of Stanford Dragon. By the proposed scheme, we obtain the numerical solution after 400 iterations (see Figure 7(c)) with the initial condition in Figure 7(b).

Figure 7: Front and back views of Stanford Dragon model: (a) initial point clouds, (b) initial condition of , and (c) numerical solution after 400 iterations.
3.2.2. Effect of

In this section, we investigate the effect of parameter on the three-dimensional volume reconstruction. The parameter makes the level set of shrink to the given points. We use the same parameters in Figure 7 except for the value. As shown in Figure 8, if the value of is small, then the surface is oversmoothed by the motion by mean curvature. On the other hand, if it is too large, then the surface is rough.

Figure 8: Front and back views of Stanford Dragon model with various . Numerical solution after 1000 iterations.

4. Conclusions

In this article, we developed an explicit hybrid numerical algorithm for the efficient 3D volume reconstruction from unorganized point clouds using a modified Allen-Cahn equation. The 3D volume reconstruction algorithm is based on the 3D binary image segmentation method. The proposed algorithm has potential to be used in various practical industry such as 3D model printing from scattered scanned data. The computational results confirmed that the algorithm is very efficient and robust in reconstructing 3D volume from point clouds.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The first author (D. Jeong) was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIP) (NRF-2017R1E1A1A03070953). The corresponding author (Junseok Kim) was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B03933243). This work was supported by the BK21 PLUS program.

References

  1. F. Remondino, “From point cloud to surface: the modeling and visualization problem,” in Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXIV-5/W10, 2003.
  2. H. Hoppe, T. DeRose, T. Duchamp, J. McDonald, and W. Stuetzle, “Surface reconstruction from unorganized points,” Computer Graphics, vol. 26, no. 2, pp. 71–78, 1992. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Kazhdan, “Reconstruction of solid models from oriented point sets,” in Proceedings of the third Eurographics Symposium on Geometry Processing, pp. 73–82, 2005.
  4. Y. Li, D. Lee, C. Lee et al., “Surface embedding narrow volume reconstruction from unorganized points,” Computer Vision and Image Understanding, vol. 121, pp. 100–107, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Li and J. Kim, “Fast and efficient narrow volume reconstruction from scattered data,” Pattern Recognition, vol. 48, no. 12, article no. 5459, pp. 4057–4069, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Yang, R. Li, Y. Xiao, and Z. Cao, “3D reconstruction from non-uniform point clouds via local hierarchical clustering,” in Proceedings of the 9th International Conference on Digital Image Processing, ICDIP 2017, China, May 2017. View at Publisher · View at Google Scholar · View at Scopus
  7. H.-K. Zhao, S. Osher, and R. Fedkiw, “Fast surface reconstruction using the level set method,” in Proceedings of the IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001, pp. 194–199, can. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Yezzi Jr., S. Kichenassamy, A. Kumar, P. Olver, and A. Tannenbaum, “A geometric snake model for segmentation of medical imagery,” IEEE Transactions on Medical Imaging, vol. 16, no. 2, pp. 199–209, 1997. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Beneš, V. r. Chalupecky, and K. Mikula, “Geometrical image segmentation by the Allen-Cahn equation,” Applied Numerical Mathematics, vol. 51, no. 2-3, pp. 187–205, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  10. V. Caselles, F. Catte, T. Coll, and F. Dibos, “A geometric model for active contours in image processing,” Numerische Mathematik, vol. 66, no. 1, pp. 1–31, 1993. View at Publisher · View at Google Scholar · View at MathSciNet
  11. V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” International Journal of Computer Vision, vol. 22, no. 1, pp. 61–79, 1997. View at Publisher · View at Google Scholar · View at Scopus
  12. T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Transactions on Image Processing, vol. 10, no. 2, pp. 266–277, 2001. View at Publisher · View at Google Scholar · View at Scopus
  13. J. Hahn and C.-O. Lee, “Geometric attraction-driven flow for image segmentation and boundary detection,” Journal of Visual Communication and Image Representation, vol. 21, no. 1, pp. 56–66, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and J. Yezzi, “Conformal curvature flows: from phase transitions to active vision,” Archive for Rational Mechanics and Analysis, vol. 134, no. 3, pp. 275–301, 1996. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. B. Zhang, W. Wang, and X. Feng, “Subspace Clustering with Sparsity and Grouping Effect,” Mathematical Problems in Engineering, vol. 2017, Article ID 4787039, 9 pages, 2017. View at Publisher · View at Google Scholar · View at Scopus
  16. Y.-T. Chen, “Medical Image Segmentation Using Independent Component Analysis-Based Kernelized Fuzzy c-Means Clustering,” Mathematical Problems in Engineering, vol. 2017, Article ID 5892039, 21 pages, 2017. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Zhang, J. Xu, and H. D. Cheng, “A Novel Fuzzy Level Set Approach for Image Contour Detection,” Mathematical Problems in Engineering, vol. 2016, Article ID 2602647, 12 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. C. Li, C. Xu, C. Gui, and M. D. Fox, “Level set evolution without re-initialization: a new variational formulation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), pp. 430–436, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  19. L. A. Vese and T. F. Chan, “A multiphase level set framework for image segmentation using the Mumford and Shah model,” International Journal of Computer Vision, vol. 50, no. 3, pp. 271–293, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. Y. Li and J. Kim, “A fast and accurate numerical method for medical image segmentation,” Journal of the Korean Society for Industrial and Applied Mathematics, vol. 14, no. 4, pp. 201–210, 2010. View at Google Scholar · View at MathSciNet
  21. A. M. Stuart and A. R. Humphries, Dynamical Systems and Numerical Analysis, vol. 2, Cambridge University Press, New York, NY, USA, 1998. View at MathSciNet
  22. Y. Li, H. G. Lee, D. Jeong, and J. Kim, “An unconditionally stable hybrid numerical method for solving the Allen-Cahn equation,” Computers & Mathematics with Applications. An International Journal, vol. 60, no. 6, pp. 1591–1606, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  23. S. M. Allen and J. W. Cahn, “A microscopic theory for antiphase boundary motion and its application to antiphase domain coarsening,” Acta Metallurgica et Materialia, vol. 27, no. 6, pp. 1085–1095, 1979. View at Publisher · View at Google Scholar · View at Scopus
  24. B. Appleton and H. Talbot, “Globally optimal geodesic active contours,” Journal of Mathematical Imaging and Vision, vol. 23, no. 1, pp. 67–86, 2005. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  25. Stanford university computer graphics laboratory, http://lightfield.stanford.edu/acq.html.