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Point set surface compression based on shape pattern analysis

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Abstract

In this work we propose an efficient algorithm for progressive point set surface compression based on shape pattern analysis. The algorithm proceeds as follows. First, the model surface is segmented into square patches according to the principal directions of the surfel. Then, the square patch is parameterized into a 2D domain and regularly resampled. After the resampling, each patch can be described as a height map. Using the height maps, we do the similarity analysis between patches. The patches which have the similar shape are classified into the same cluster, called a shape pattern. For patches in the same shape pattern, a representative patch is computed; then each patch can be represented as the representative patch plus an error correction. When decoding, the profile of the model can be quickly reconstructed using the representative patches and transformation parameters. Then with the decoding of the error image, the model can be gradually refined, implementing progressive compression of 3D point-based models.

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References

  1. Adams R, Bischof L (1994) Seeded region growing[J. IEEE Trans Pattern Anal Mach Intell 16(6):641–647

    Article  Google Scholar 

  2. Alexa M, Behr J (2001) Cohen-or D, et al. point set surfaces [C] //proceedings of the conference on Visualization’01. IEEE computer. Society:21–28

  3. Benabdelkader C, Cutler RG, Davis LS (2010) Gait recognition using image self-similarity[J]. Eurasip journal on applied. Signal Process 2004(4):572–585

    Google Scholar 

  4. Botsch M, Wiratanaya A, Kobbelt L (2002) Efficient high quality rendering of point sampled geometry[C]// Proceedings of the 13th Eurographics workshop on Rendering. Eurographics Association:53–64

  5. Chen C (2004) An enhanced generalized Lloyd algorithm[J. IEEE Signal Processing Letters 11(2):167–170

    Article  Google Scholar 

  6. Chen D, Chiang Y J, Memon N. (2005) Lossless compression of point-based 3D models [C]//Pacific Graphics. 124–126.

  7. Deselaers T, Ferrari V (2010) Global and efficient self-similarity for object classification and detection[C]// IEEE Conference on Computer Vision & Pattern Recognition. IEEE 1633–1640

  8. Dong J, Qi L, Ren J et al (2005) Self-similarity Based Editing of 3D Surface Textures[J]. Proceedings of International Workshop on Texture Analysis & Synthesis 6(2):209–212

    Google Scholar 

  9. Fleishman S, Cohen-Or D, Alexa M et al (2003) Progressive point set surfaces [J]. ACM Transactions on Graphics (TOG) 22(4):997–1011

    Article  Google Scholar 

  10. Goldfeather J, Interrante V. (2004) A novel cubic-order algorithm for approximating principal direction vectors[C]// Acm Transactions on Graphics. 45–63.

  11. Gumhold S, Kami Z, Isenburg M et al (2005) Predictive point-cloud compression [C]//ACM SIGGRAPH 2005 Sketches. ACM:137

  12. He C, Zhang Y, Li X. (2013) Principal Directions Estimation on Point Set Surfaces[C]// IEEE Seventh International Conference on Image and Graphics. 217–220.

  13. Huang J, You S (2012) Point cloud matching based on 3D self-similarity[C]// computer vision and pattern recognition workshops (CVPRW), 2012 I.E. computer society conference on. IEEE:41–48

  14. Huang Y, Peng J, Kuo CCJ et al (2006) Octree-based progressive geometry coding of point clouds[C]// Proceedings of the 3rd Eurographics/IEEE VGTC conference on Point-Based Graphics. Eurographics Association 103–110

  15. Huang Y, Peng J, Kuo CCJ et al (2008) A generic scheme for progressive point cloud coding[J]. Visualization and Computer Graphics, IEEE Transactions on 14(2):440–453

    Article  Google Scholar 

  16. Huang J, You S, Zhao J (2011) Multimodal image matching using self similarity [C]// IEEE applied imagery pattern recognition workshop. IEEE:1–6

  17. Hubo E, Mertens T, Haber T et al (2006) The quantized kd-tree: efficient ray tracing of compressed point clouds[C]// interactive ray tracing 2006, IEEE symposium on. IEEE:105–113

  18. Hubo E, Mertens T, Haber T et al (2008) Self-similarity based compression of point set surfaces with application to ray tracing [J]. Computers & Graphics 32(2):221–234

    Article  Google Scholar 

  19. Ioannis K, Kuo CCJ, Zhang Z (1995) Fast generalized Lloyd iteration for vector quantizers (VQ) codebook design[C]// IS&T/SPIE’s Symposium on Electronic Imaging. Science & Technology:76–87

  20. I. N. Junejo, E. Dexter, I. Laptev, and P. Perez. (2010) View-independent action recognition from temporal self-similarities[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 99, no. PrePrints.

  21. Kalaiah A, Varshney A (2005) Statistical geometry representation for efficient transmission and rendering [J]. ACM Transactions on Graphics (TOG) 24(2):348–373

    Article  Google Scholar 

  22. Kim Y, Bilgin A (2013) Video compressed sensing using iterative self-similarity modeling and residual reconstruction[J]. Journal of Electronic Imaging 22(2):381–388

    Article  Google Scholar 

  23. Kim S, Ryu S, Ham B et al (2014) Local self-similarity frequency descriptor for multispectral feature matching [C]// IEEE international conference on image processing. IEEE:5746–5750

  24. Kruger J, Schneider J, Westermann R (2005) Duodecim-a structure for point scan compression and rendering[C]//point-based graphics, 2005. Eurographics/IEEE VGTC symposium proceedings. IEEE:99–146

  25. Li J, Lu Z, Zeng G et al (2014) Similarity-aware patchwork assembly for depth image super-resolution[C]// computer vision and pattern recognition. IEEE:3374–3381

  26. Liang M, Du J, Li L (2015) Video super-resolution reconstruction based on correlation learning and spatio-temporal nonlocal similarity[J]. Multimedia Tools & Applications 1–29

  27. Merry B, Marais P, Gain J (2006) Compression of dense and regular point clouds[C]//Computer Graphics Forum. Blackwell Publishing Ltd 25(4):709–716

    Google Scholar 

  28. Ochotta T, Saupe D (2004) Compression of point-based 3d models by shape-adaptive wavelet coding of multi-height fields [C]// Proceedings of the First Eurographics conference on Point-Based Graphics. Eurographics Association 103–112

  29. Ochotta T, Saupe D (2008) Image-Based Surface Compression [C] //Computer graphics forum. Blackwell Publishing Ltd 27(6):1647–1663

    MATH  Google Scholar 

  30. Pajarola R. (2003) Efficient level-of-details for point based rendering[C]//Proceedings IASTED Computer Graphics and Imaging Conference (CGIM). 141–146.

  31. Park SB, Lee SU (2009) Multiscale representation and compression of 3-D point data [J]. Multimedia, IEEE Transactions on 11(1):177–183

    Article  Google Scholar 

  32. Peng J, Kim C, Kuo C (2005) Technologies for 3D mesh compression: a survey [J. J Vis Commun Image Represent 16(6):688–733

    Article  Google Scholar 

  33. Pfister H, Zwicker M, Van Baar J, et al. (2000) Surfels: Surface elements as rendering primitives [C]//Proceedings of the 27th annual conference on Computer graphics and interactive techniques. ACM Press/Addison-Wesley Publishing Co., 335–342.

  34. Razdan A, Bae MS (2005) Curvature estimation scheme for triangle meshes using biquadratic Bézier patches[J. Comput Aided Des 37(14):1481–1491

    Article  MATH  Google Scholar 

  35. Rusinkiewicz S, Levoy M. (2000) QSplat: A multiresolution point rendering system for large meshes [C]// Proceedings of the 27th annual conference on Computer graphics and interactive techniques. ACM Press/Addison-Wesley Publishing Co., 343–352.

  36. Schnabel R, Klein R (2006) Octree-based point-cloud compression[C] //Symposium on point-based graphics. The Eurographics Association 111–120

  37. Schnabel R, Moser S, Klein R (2008) Fast vector quantization for efficient rendering of compressed point-clouds [J]. Computers & Graphics 32(2):246–259

    Article  Google Scholar 

  38. Shapiro J (1993) Embedded image coding using zerotrees of wavelet coefficients[J. IEEE Trans Signal Process 41(12):3445–3462

    Article  MATH  Google Scholar 

  39. Shechtman E, Irani M (2007) Matching Local Self-Similarities across Images and Videos[C]// IEEE Conference on Computer Vision & Pattern Recognition. IEEE 1–8

  40. Smith J, Petrova G, Schaefer S (2012) Progressive encoding and compression of surfaces generated from point cloud data. Computers & Graphics 36(5):341–348

    Article  Google Scholar 

  41. Sun C, Junejo IN, Tappen M et al (2015) Exploring sparseness and self-similarity for action recognition.[J. IEEE Trans Image Process 24(8):2488–2501

    Article  MathSciNet  Google Scholar 

  42. Taubin G (1995) Estimating the tensor of curvature of a surface from a polyhedral approximation[C]// International Conference on Computer Vision (ICCV). Proceedings. 1995:902–907

    Google Scholar 

  43. Waschbusch M, Gross M, Eberhard F et al (2004) Progressive compression of point sampled models [C]// Proceedings of the First Eurographics conference on Point-Based Graphics. Eurographics Association 95–103

  44. Witten IH, Neal RM, Cleary JG (1987) Arithmetic coding for data compression[J. Commun ACM 30(6):520–540

    Article  Google Scholar 

  45. Zwicker M, Pauly M, Knoll O et al (2002) Pointshop 3D: an interactive system for pointbased surface editing[J]. ACM Transactions on Graphics (TOG) 21(3):322–329

    Article  Google Scholar 

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Acknowledgments

We thank the workgroup of Pointshop3D for providing software and point-based models. This work is supported by the Science and Technology Development Plan Project of Shandong Province (2012G0020127), the Science and Technology Development Plan Project of Weifang City (2015GX009) and Doctoral Research Foundation of Weifang University (2015BS12).

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Authors and Affiliations

  1. College of Computer Engineering, Weifang University, Weifang, Shandong, 261061, China

    Chen He & Lei Wang

  2. Geotechnical & Structural Engineering Research Center, Shandong University, Jinan, Shandong, 250061, China

    Lingqiang Ran

  3. School of Computer Science and Technology, Shandong University, Jinan, Shandong, 250101, China

    Xueqing Li

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  1. Chen He
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  2. Lingqiang Ran
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  3. Lei Wang
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  4. Xueqing Li
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Correspondence to Chen He.

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He, C., Ran, L., Wang, L. et al. Point set surface compression based on shape pattern analysis. Multimed Tools Appl 76, 20545–20565 (2017). https://doi.org/10.1007/s11042-016-3991-0

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  • DOI: https://doi.org/10.1007/s11042-016-3991-0

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