Skip to main content
SpringerLink
Log in

A Slice-Guided Method of Indoor Scene Structure Retrieving

  • Conference paper
  • First Online:
Book cover E-Learning and Games (Edutainment 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11462))

Included in the following conference series:

  • 1224 Accesses

Abstract

The structure information of indoor scene is necessary for a robot who works in a room. In order to achieve structure of an indoor scene, a slice-guided method of indoor scene structure retrieving is proposed in this paper. We present a slicing based approach that transforms three-dimensional (3D) segmentations into two-dimensional (2D) segmentation and segments different kinds of primitive shapes while keeping the global topology structure of the indoor scene. The global topology structure is represented by a graph. The graph is compared with the given indoor scene template. The matched objects and the topology relation between them are finally presented. Our experiment results show that the proposed method performs well on several typical indoor scenes, even if some data are missing.

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 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Nan, L.L., Xie, K., Sharf, A.: A search-classify approach for cluttered indoor scene understanding. ACM Trans. Graph. 31(6), 1–10 (2012)

    Article  Google Scholar 

  2. Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33715-4_54

    Chapter  Google Scholar 

  3. Socher, R., Huval, B., Bhat, B., et al.: Convolutional-recursive deep learning for 3D object classification. In: Neural Information Processing Systems Conference and Workshop, NIPS, Nevada, pp. 665–673 (2012)

    Google Scholar 

  4. Xu, K., Huang, H., Shi, Y., et al.: Auto scanning for coupled scene reconstruction and proactive object analysis. ACM Trans. Graph. 34(6), 177 (2015)

    Article  Google Scholar 

  5. Xiong, X.H., Huber, D.: Using context to create semantic 3D models of indoor environments. In: British Machine Vision Conference, BMVC 2010, Aberystwyth, UK, pp. 1–11 (2010)

    Google Scholar 

  6. Anand, A., Koppula, H.S., Joachims, T., et al.: Contextually guided semantic labeling and search for 3D point clouds. Int. J. Robot. Res. 32(1), 19–34 (2011)

    Article  Google Scholar 

  7. Jiang, Y., Koppula, H., Saxena, A.: Hallucinated humans as the hidden context for labeling 3D scenes. In: Computer Vision and Pattern Recognition, pp. 2993–3000, IEEE press, Portland (2013)

    Google Scholar 

  8. Savva, M., Chang, A.X., Hanrahan, P., et al.: SceneGrok: inferring action maps in 3D environments. ACM Trans. Graph. 33(6) (2014)

    Article  Google Scholar 

  9. Zhang, Y., Xu, W., Tong, Y., Zhou, K.: Online structure analysis for real-time indoor scene reconstruction. ACM Trans. Graph. 34(5), 159 (2015)

    Article  Google Scholar 

  10. Wang, J., Xie, Q., Xu, Y., et al.: Cluttered indoor scene modeling via functional part-guided graph matching. Comput. Aided Geom. Des. 43(C), 82–94 (2016)

    Article  MathSciNet  Google Scholar 

  11. Hao, W., Wang, Y.H.: Structure-based object detection from scene point clouds. Neurocomputing 191, 148–160 (2016)

    Article  Google Scholar 

  12. Schnabel, R., Wahl, R., Wessel, R., et al.: Shape recognition in 3D point-clouds, vol. 272, no. 1, pp. 512–520. Václav Skala - UNION Agency (2008)

    Google Scholar 

  13. Wang, Y., Wang, L., Hao, W, et al.: A novel slicing-based regularization method for raw point clouds in visible IoT, pp. 1–9 (2000)

    Google Scholar 

  14. Guru, D.S., Shekar, B.H., Nagabhushan, P.: A simple and robust line detection algorithm based on small eigenvalue analysis. Pattern Recogn. Lett. 25(1), 1–13 (2004)

    Article  Google Scholar 

  15. Leordeanu, M., Hebert, M.: Unsupervised learning for graph matching. Int. J. Comput. Vis. 96(1), 28–45 (2012)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgement

This study is supported by the National Key Research and Development Program of China No. 2018YFB1004905; the Nature Science Foundation of China under Grant No. 61472319, 61872291, 61871320; and in part by Shaanxi Science Research Plan under Grant No. 2017JQ6023; in part by Scientific Research Program Funded by Shaanxi Provincial Education Department 18JS077.

Author information

Authors and Affiliations

  1. Xi’an University of Technology, South Jinhua Road no. 5, Xi’an, 710032, China

    Lijuan Wang, Yinghui Wang & Xiaojuan Ning

  2. Xi’an Technology University, Mid Xuefu Road no. 2, Xi’an, 710021, China

    Lijuan Wang

  3. Shaanxi Key Laboratory of Network Computing and Security Technology, South Jinhua Road no. 5, Xi’an, 710032, China

    Yinghui Wang & Xiaojuan Ning

  4. Booking.Com, B.V. Vijzelstraat 66-80, 1017HL, Amsterdam, Netherlands

    Ningna Wang

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Ke Lv

  6. School of Engineering and Applied Science, George Washington University, Washington DC, 20052, USA

    Liangyi Huang

Authors
  1. Lijuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yinghui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ningna Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaojuan Ning
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ke Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Liangyi Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yinghui Wang .

Editor information

Editors and Affiliations

  1. Liverpool John Moores University, Liverpool, UK

    Abdennour El Rhalibi

  2. Hangzhou Normal University, Hangzhou, China

    Zhigeng Pan

  3. Xi’an University of Technology, Xi’an, China

    Haiyan Jin

  4. Hangzhou Normal University, Hangzhou, China

    Dandan Ding

  5. Pontificia Universidad Javeriana, Cali, Colombia

    Andres A. Navarro-Newball

  6. Xi’an University of Technology, Xi’an, China

    Yinghui Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, L., Wang, Y., Wang, N., Ning, X., Lv, K., Huang, L. (2019). A Slice-Guided Method of Indoor Scene Structure Retrieving. In: El Rhalibi, A., Pan, Z., Jin, H., Ding, D., Navarro-Newball, A., Wang, Y. (eds) E-Learning and Games. Edutainment 2018. Lecture Notes in Computer Science(), vol 11462. Springer, Cham. https://doi.org/10.1007/978-3-030-23712-7_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-23712-7_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23711-0

  • Online ISBN: 978-3-030-23712-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation