Skip to main content
SpringerLink
Log in

3D object simplification using chain code-based point clouds

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This work aims to obtain a sequence of 3D point clouds associated with a 3D object that reduces the volume data and preserves the shape of the original object. The sequence contains point clouds that give different simplifications of the object, from a very fine-tuned representation to a simple and sparse one. Such a sequence is important because it satisfies different needs, from a faithful representation with a low reduction of points to a significant data reduction that only preserves the main properties of the object. We construct the sequence in the following way. We first obtain a voxelization of the original 3D object. Then, we organize the voxels by slices to get a single chain code that represents the original 3D object. The point clouds depend on the key points of the chain code. The Hausdorff distance and the average geometric error prove that the point clouds are invariant under rigid rotations and maintain the shape of the object. Our results indicate that the proposed method has an average efficiency of 60% regarding the state-of-the-art simplification methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Cao C, Preda M, Zaharia T (2019) 3d point cloud compression: a survey. In: The 24th international conference on 3d web technology. Web3d ’19, pp 1–9, Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3329714.3338130

  2. CGAL Computational Geometry Algorithms Library. http://www.cgal.org/

  3. Chen Z, Liu L (2021) Navigable space construction from sparse noisy point clouds. IEEE Robot Autom Lett 6(3):4720–4727. https://doi.org/10.1109/LRA.2021.3068939

    Article  Google Scholar 

  4. Chen S, Tian D, Feng C, Vetro A, Kovačević J (2017) Fast resampling of three-dimensional point clouds via graphs. IEEE Trans Signal Process 66(3):666–681

    Article  MathSciNet  MATH  Google Scholar 

  5. Chen S, Tian D, Feng C, Vetro A, Kovačević J (2018) Fast resampling of three-dimensional point clouds via graphs. IEEE Trans Signal Process 66(3):666–681. https://doi.org/10.1109/TSP.2017.2771730

    Article  MathSciNet  MATH  Google Scholar 

  6. El Sayed AR, El Chakik A, Alabboud H, Yassine A (2019) An efficient simplification method for point cloud based on salient regions detection. RAIRO-Oper Res 53(2):487–504

    Article  MathSciNet  MATH  Google Scholar 

  7. Fan H, Yang Y, Kankanhalli M (2021) Point 4d transformer networks for spatio-temporal modeling in point cloud videos. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 14204–14213

  8. Fan H, Yu X, Yang Y, Kankanhalli M (2021) Deep hierarchical representation of point cloud videos via spatio-temporal decomposition. IEEE Trans Pattern Anal Mach Intell

  9. Freeman H (1961) On the encoding of arbitrary geometric configurations. IRE Trans Electron Comput EC-10(2):260–268. https://doi.org/10.1109/TEC.1961.5219197

    Article  MathSciNet  Google Scholar 

  10. Garcia DC, Fonseca TA, Ferreira RU, de Queiroz RL (2020) Geometry coding for dynamic voxelized point clouds using octrees and multiple contexts. IEEE Trans Image Process 29:313–322. https://doi.org/10.1109/TIP.2019.2931466

    Article  MathSciNet  MATH  Google Scholar 

  11. Golla T, Kneiphof T, Kuhlmann H, Weinmann M, Klein R (2020) Temporal upsampling of point cloud sequences by optimal transport for plant growth visualization. In: Computer Graphics Forum, vol 39, pp 167–179. Wiley Online Library

  12. Good CD, Johnsrude IS, Ashburner J, Henson RNA, Friston KJ, Frackowiak RSJ (2001) A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14(1):21–36. https://doi.org/10.1006/nimg.2001.0786

    Article  Google Scholar 

  13. Gu S, Hou J, Zeng H, Yuan H, Ma K-K (2019) 3d point cloud attribute compression using geometry-guided sparse representation. IEEE Trans Image Process PP:1–1. https://doi.org/10.1109/TIP.2019.2936738https://doi.org/10.1109/TIP.2019.2936738

    MATH  Google Scholar 

  14. Guo Y, Sohel F, Bennamoun M, Wan J, Lu M (2015) A novel local surface feature for 3d object recognition under clutter and occlusion. Inf Sci 293:196–213. https://doi.org/10.1016/j.ins.2014.09.015

    Article  Google Scholar 

  15. Huang H, Li D, Zhang H, Ascher U, Cohen-Or D (2009) Consolidation of unorganized point clouds for surface reconstruction. ACM Trans Graph (TOG) 28(5):1–7

    Article  Google Scholar 

  16. Huang T, Liu Y (2019) 3d point cloud geometry compression on deep learning. In: MM ’19: The 27Th ACM international conference on multimedia, pp 890–898. https://doi.org/10.1145/3343031.3351061

  17. Huiyan H, Xie H, Fusheng S, Chunyan H (2015) Point cloud simplification with preserved edge based on normal vector. Optik - Int J Light Electron Opt 126(19):2157–2162. https://doi.org/10.1016/j.ijleo.2015.05.092https://doi.org/10.1016/j.ijleo.2015.05.092

    Article  Google Scholar 

  18. Ji C, Li Y, Fan J, Lan S (2019) A novel simplification method for 3d geometric point cloud based on the importance of point. IEEE Access 7:129029–129042

    Article  Google Scholar 

  19. Junkun Q, Wei H, Zongming G (2019) Feature preserving and uniformity-controllable point cloud simplification on graph. In: 2019 IEEE International conference on multimedia and expo (ICME), pp 284–289. https://doi.org/10.1109/ICME.2019.00057

  20. Kansal S, Madan J, Singh A (2013) A systematic approach for cad model generation of hole features from point cloud data. In: 2013 3rd IEEE International Advance Computing Conference (IACC), pp 1385–1393. IEEE

  21. Kehl W, Milletari F, Tombari F, Ilic S, Navab N (2016) Deep learning of local rgb-d patches for 3d object detection and 6d pose estimation. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer vision – ECCV 2016, pp 205–220. Springer, Cham

  22. Kim J, Im J, Rhyu S, Kim K (2020) 3d motion estimation and compensation method for video-based point cloud compression. IEEE Access 8:83538–83547

    Article  Google Scholar 

  23. Klette R, Rosenfeld A (2004) Digital geometry: geometric methods for digital picture analysis. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA

  24. Krüsi P, Furgale P, Bosse M, Siegwart R (2017) Driving on point clouds: motion planning, trajectory optimization, and terrain assessment in generic nonplanar environments. J Field Robot 34(5):940–984

    Article  Google Scholar 

  25. Leal E, Sanchez-Torres G, Branch-Bedoya JW, Abad F, Leal N (2021) A saliency-based sparse representation method for point cloud simplification. Sensors 21(13):4279

    Article  Google Scholar 

  26. Lee M-y, Lee S-h, Jung K-d, Lee S-h, Kwon S-c (2021) A novel preprocessing method for dynamic point-cloud compression. Appl Sci 11 (13):5941

    Article  Google Scholar 

  27. Liu H, Cong Y, Yang C, Tang Y (2019) Efficient 3d object recognition via geometric information preservation. Pattern Recogn 92:135–145. https://doi.org/10.1016/j.patcog.2019.03.025

    Article  Google Scholar 

  28. Liu Z, Li Q, Chen X, Wu C, Ishihara S, Li J, Ji Y (2021) Point cloud video streaming: challenges and solutions. IEEE Netw 35(5):202–209. https://doi.org/10.1109/MNET.101.2000364

    Article  Google Scholar 

  29. Liu X, Yan M, Bohg J (2019) Meteornet: deep learning on dynamic 3d point cloud sequences. In: Proceedings of the IEEE/CVF international conference on computer vision (ICCV)

  30. Liu YK, žalik B (2005) An efficient chain code with huffman coding. Pattern Recogn 38(4):553–557. https://doi.org/10.1016/j.patcog.2004.08.017

    Article  Google Scholar 

  31. Min P (Unknown Month 2004) Binvox. http://www.patrickmin.com/binvox. Accessed: 18 Feb 2022 and 09 Aug 2022

  32. Muhammad S, Joono C, Y K, H C (2019) Fractal bubble algorithm for simplification of 3d point cloud data. J Intell and Fuzzy Syst 37:7815–7830. https://doi.org/10.3233/jifs-182742

    Article  Google Scholar 

  33. Nguyen CHP, Choi Y (2018) Comparison of point cloud data and 3d cad data for on-site dimensional inspection of industrial plant piping systems. Autom Constr 91:44–52

    Article  Google Scholar 

  34. Ning X, Wang Y, Meng W, Zhang X (2016) Optimized shape semantic graph representation for object understanding and recognition in point clouds. Opt Eng 55(10):1–14. https://doi.org/10.1117/1.OE.55.10.103111https://doi.org/10.1117/1.OE.55.10.103111

    Article  Google Scholar 

  35. Nooruddin FS, Turk G (2003) Simplification and repair of polygonal models using volumetric techniques. IEEE Trans Vis Comput Graph 9(2):191–205

    Article  Google Scholar 

  36. Pauly M, Gross M, Kobbelt LP (2002) Efficient simplification of point-sampled surfaces. In: IEEE Visualization, 2002. VIS 2002, pp 163–170. https://doi.org/10.1109/VISUAL.2002.1183771

  37. Sánchez-Cruz H, Tapia-Dueñas OA, Cuevas F (2019) Polygonal approximation using a multiresolution method and a context-free grammar. In: Carrasco-Ochoa JA, Martínez-Trinidad JF, Olvera-López JA, Salas J (eds) Pattern Recognition, pp 261–270. Springer, Cham. https://doi.org/10.1007/978-3-030-21077-9_24

  38. Shah SAA, Bennamoun M, Boussaid F (2016) A novel feature representation for automatic 3d object recognition in cluttered scenes. Neurocomputing 205:1–15. https://doi.org/10.1016/j.neucom.2015.11.019https://doi.org/10.1016/j.neucom.2015.11.019

    Article  Google Scholar 

  39. Shah SAA, Bennamoun M, Boussaid F (2017) Keypoints-based surface representation for 3d modeling and 3d object recognition. Pattern Recogn 64:29–38. https://doi.org/10.1016/j.patcog.2016.10.028

    Article  Google Scholar 

  40. Shiquan Q, Kun Z, Kai G (2019) Algorithm for point cloud compressing based on geometrical features. Int J Performability Eng 15:782

    Google Scholar 

  41. Siva S, Nahman Z, Zhang H (2020) Voxel-based representation learning for place recognition based on 3d point clouds. In: 2020 IEEE/RSJ International conference on intelligent robots and systems (IROS), pp 8351–8357. https://doi.org/10.1109/IROS45743.2020.9340992

  42. Song H, Feng H-Y (2007) A point cloud simplification algorithm for mechanical part inspection. International Federation for Information Processing Digital Library; Information Technology For Balanced Manufacturing Systems; 220. https://doi.org/10.1007/978-0-387-36594-7_49

  43. Song H, Feng H-Y (2007) Point-cloud simplification with bounded geometric deviations. Int J Comput Appl Technol 30(4):236–244. https://doi.org/10.1504/IJCAT.2007.017235

    Article  Google Scholar 

  44. Song H, Feng H-Y (2009) A progressive point cloud simplification algorithm with preserved sharp edge data. Int J Advan Manuf Technol 45:583–592. https://doi.org/10.1007/s00170-009-1980-4

    Article  Google Scholar 

  45. Szeliski R (2010) Computer vision: algorithms and applications, 1st edn. Springer, Berlin

    MATH  Google Scholar 

  46. Taha AA, Hanbury A (2015) An efficient algorithm for calculating the exact hausdorff distance. IEEE Trans Pattern Anal Mach Intell 37(11):2153–2163. https://doi.org/10.1109/TPAMI.2015.2408351

    Article  Google Scholar 

  47. Tangelder JWH, Veltkamp RC (2004) A survey of content based 3d shape retrieval methods. In: Proceedings Shape Modeling Applications, 2004, pp 145–156. https://doi.org/10.1109/SMI.2004.1314502

  48. Tapia-Dueñas OA, Sánchez-Cruz H (2021) Context-free grammars to detect straight segments and a novel polygonal approximation method. Signal Process Image Commun 116080:91. https://doi.org/10.1016/j.image.2020.116080

    Google Scholar 

  49. Thanou D, Chou PA, Frossard P (2016) Graph-based compression of dynamic 3d point cloud sequences. IEEE Trans Image Process 25(4):1765–1778. https://doi.org/10.1109/TIP.2016.2529506

    Article  MathSciNet  MATH  Google Scholar 

  50. Toledo L, De Gyves O, Rudomín I (2014) Hierarchical level of detail for varied animated crowds. Vis Comput 30(6):949–961

    Article  Google Scholar 

  51. Vogiatzis G, Hernández C (2011) Video-based, real-time multi-view stereo. Image Vis Comput 29(7):434–441. https://doi.org/10.1016/j.imavis.2011.01.006

    Article  Google Scholar 

  52. Wang L, Xu Y, Li Y (2018) A voxel-based 3d building detection algorithm for airborne lidar point clouds. J Indian Soc Remote Sens 47:349–358

    Article  Google Scholar 

  53. Wei J, Xu M, Xiu H (2020) A point clouds fast thinning algorithm based on sample point spatial neighborhood. J Inform Process Syst 16(3):688–698

    Google Scholar 

  54. Yang Y, Li M, Ma X (2020) An advanced vehicle body part inspection scheme based on scattered point cloud data. Appl Sci 10(15):5379

    Article  Google Scholar 

  55. Yang Y, Zhuang Y, Pan Y (2021) Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies. Front Inform Technol Electron Eng 22(12):1551–1558

    Article  Google Scholar 

  56. Zou Y, Wang X, Zhang T, Liang B, Song J, Liu H (2018) Broph: An efficient and compact binary descriptor for 3d point clouds. Pattern Recogn 76:522–536. https://doi.org/10.1016/j.patcog.2017.11.029

    Article  Google Scholar 

  57. Zuquete Guarato A, Quinsat Y, Mehdi-Souzani C, Lartigue C, Sura E (2018) Conversion of 3D scanned point cloud into a voxel-based representation for crankshaft mass balancing. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-017-1319-5

Download references

Funding

Osvaldo A. Tapia-Dueñas was partially supported by CONACyT, CVU 781156. Hermilo Sánchez-Cruz was partially supported by Universidad Autónoma de Aguascalientes, grant PII22-5. Hiram H. López was partially supported by an AMS–Simons Travel Grant.

Author information

Authors and Affiliations

  1. Departamento de Ciencias de la Computación, Universidad Autónoma de Aguascalientes, Aguascalientes, 20100, Aguascalientes, México

    Osvaldo A. Tapia-Dueñas & Hermilo Sánchez-Cruz

  2. Department of Mathematics and Statistics, Cleveland State University, Cleveland, 44115, OH, USA

    Hiram H. López

Authors
  1. Osvaldo A. Tapia-Dueñas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hermilo Sánchez-Cruz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hiram H. López
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hermilo Sánchez-Cruz.

Ethics declarations

Conflict of Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tapia-Dueñas, O.A., Sánchez-Cruz, H. & López, H.H. 3D object simplification using chain code-based point clouds. Multimed Tools Appl 82, 9491–9515 (2023). https://doi.org/10.1007/s11042-022-13588-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13588-3

Keywords

Search

Navigation