Skip to main content
SpringerLink
Log in

Image-Based Physics Rendering for 3D Surface Reconstruction: A Survey

  • Conference paper
  • First Online:
3D Imaging—Multidimensional Signal Processing and Deep Learning

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 297))

  • 520 Accesses

Abstract

Obtaining 3D surface information and physical material information of an object from images is an essential research prospect in computer vision and computer graphics. Image-based 3D reconstruction is to extract the 3D depth information of the scene and objects from single or multiple images through specific algorithms to reconstruct the 3D model of objects or locations with robust realism, which has fast reconstruction speed, simple equipment, realistic effect, and minor technical data, which can better realize the virtualization of natural objects.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Song, P., Wu, X., Wang, M.Y.: A robust and accurate method for visual hull computation. In: 2009 International Conference on Information and Automation, pp. 784–789. IEEE (2009)

    Google Scholar 

  2. Rakitina, E., Rakitin, I., Staleva, V., Arnaoutoglou, F., Koutsoudis, A., Pavlidis, G.: An overview of 3D laser scanning technology. In: Proceedings of the International Scientific Conference (2008)

    Google Scholar 

  3. Hosseini, S.J., Araujo, H.: A ToF-aided approach to 3D mesh-based reconstruction of isometric surfaces. In: International Conference on Pattern Recognition Applications and Methods, pp. 146–161. Springer, Cham (2014)

    Google Scholar 

  4. Hou, Z., Su, X., Zhang, Q.: 3D shape compression based on virtual structural light encoding. Acta Optica Sinica 31(5) (2011)

    Google Scholar 

  5. Martin, W.N., Aggarwal, J.K.: Volumetric descriptions of objects from multiple views. IEEE Trans. Pattern Anal. Mach. Intell. 5(2), 150–158 (1983)

    Article  Google Scholar 

  6. Tomasi, C., Kanade, T.: Shape and motion from image streams under orthography: a factorization method. Int. J. Comput. Vision 9(2), 137–154 (1992)

    Article  Google Scholar 

  7. Witkin, A.P.: Recovering surface shape and orientation from texture. Artif. Intell. 17(1–3), 17–45 (1981)

    Article  Google Scholar 

  8. Nayar, S.K., Nakagawa, Y.: Shape from focus: An effective approach for rough surfaces. In: Proceedings IEEE International Conference on Robotics and Automation, pp. 218–225. IEEE (1990)

    Google Scholar 

  9. Woodham, R.J.: Photometric stereo: a reflectance map technique for determining surface orientation from image intensity. In: Image Understanding Systems and Industrial Applications I, vol. 155, pp. 136–143. International Society for Optics and Photonics (1979)

    Google Scholar 

  10. Chen, X.R., Cai, P., Shi, W.K.: The latest development of optical non-contact 3D profile measurement. Opt. Precis. Eng. 10(5), 528–532 (2002)

    Google Scholar 

  11. Kolmogorov, V., Zabih, R.: Multi-camera scene reconstruction via graph cuts. In: European Conference on Computer Vision, pp. 82–96. Springer, Berlin, Heidelberg (2002)

    Google Scholar 

  12. Zabulis, X., Daniilidis, K.J.I.: Multi-camera reconstruction based on surface normal estimation and best viewpoint selection. In: Proceedings. 2nd International Symposium on 3D Data Processing, Visualization and Transmission, 3DPVT 2004, pp. 733–740. IEEE (2004)

    Google Scholar 

  13. Lange, R., Seitz, P.: Solid-state time-of-flight range camera. IEEE J. Q. Electron. 37(3), 390–397 (2001)

    Article  Google Scholar 

  14. Ringaby, E., Forssén, P. E.: Scan rectification for structured light range sensors with rolling shutters. In: 2011 International Conference on Computer Vision, ICCV 2011, pp. 1575–1582. IEEE (2011)

    Google Scholar 

  15. Laurentini, A.: The visual hull concept for silhouette-based image understanding. IEEE Trans. Pattern Anal. Mach. Intell. 16(2), 150–162 (1994)

    Article  Google Scholar 

  16. Warren, P.A., Mamassian, P.: Recovery of surface pose from texture orientation statistics under perspective projection. Biol. Cybern. 103(3), 199–212 (2010)

    Article  Google Scholar 

  17. Schmid, K., Hirschmüller, H.: Stereo Vision. Icra (2013)

    Google Scholar 

  18. Kim, Y.M., Theobalt, C., Diebel, J., Kosecka, J., Miscusik, B., Thrun, S.: Multi-view image and ToF sensor fusion for dense 3D reconstruction. In: IEEE International Conference on Computer Vision Workshops, pp. 1542–1549 (2009)

    Google Scholar 

  19. Santo, H., Samejima, M., Sugano, Y., Shi, B., Matsushita, Y.: Deep Photometric Stereo Network. In: IEEE International Conference on Computer Vision Workshop, pp. 501–509 (2017)

    Google Scholar 

  20. Zbontar, J., LeCun, Y.: Stereo matching by training a convolutional neural network to compare image patches. J. Mach. Learn. Res. 17(1), 2287–2318 (2016)

    MATH  Google Scholar 

  21. Niu, C., Li, J., Xu, K.: Im2Struct: recovering 3D shape structure from a single RGB image. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4521–4529 (2018)

    Google Scholar 

  22. Chen, G. Y., Han, K., Wong, K.K.: TOM-Net: learning transparent object matting from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9233–9241 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Yunnan Key Lab of Optic-Electronic Information Technology, Yunnan Normal Universit, Kunming, Yunnan, China

    Da Fang, Zhibao Qin, Shaojun Liang & Renbo Luo

Authors
  1. Da Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhibao Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shaojun Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Renbo Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Renbo Luo .

Editor information

Editors and Affiliations

  1. KES International, Shoreham-by-Sea, UK

    Lakhmi C. Jain

  2. Technical University of Sofia, Sofia, Bulgaria

    Roumen Kountchev

  3. Yunnan Normal University, Kunming, China

    Yonghang Tai

  4. TK Engineering, bulgaria, Bulgaria

    Roumiana Kountcheva

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fang, D., Qin, Z., Liang, S., Luo, R. (2022). Image-Based Physics Rendering for 3D Surface Reconstruction: A Survey. In: Jain, L.C., Kountchev, R., Tai, Y., Kountcheva, R. (eds) 3D Imaging—Multidimensional Signal Processing and Deep Learning. Smart Innovation, Systems and Technologies, vol 297. Springer, Singapore. https://doi.org/10.1007/978-981-19-2448-4_13

Download citation

Publish with us

Policies and ethics

Search

Navigation