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Reconstruction of 3D Models Consisting of Line Segments

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Computer Vision – ACCV 2016 Workshops (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10117))

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Abstract

Reconstruction of three-dimensional (3D) models from images is currently one of the most important areas of research in computer vision. In this paper, we propose a method to recover 3D models using the minimum number of line segments. By using structure-from-motion, the proposed method first recovers a 3D model of line segments detected from an input image sequence. We then detect overlapping 3D line segments that redundantly represent a single line structure so that the number of 3D line segments representing the target scene can be reduced without losing the detailed geometry of the structure. We apply matching and depth information to remove redundant line segments from the model while keeping the necessary segments. In experiments, we confirm that the proposed method can greatly reduce the number of line segments. We also demonstrate that the accuracy and computational time for camera pose estimation can be significantly improved with the 3D line segment model recovered by the proposed method. Moreover, we have applied the proposed method to see through occluded areas.

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References

  1. Agarwal, S., Snavely, N., Simon, I., Seitz, S.M., Szeliski, R.: Building Rome in a day. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 72–79 (2009)

    Google Scholar 

  2. Sturm, P., Triggs, B.: A factorization based algorithm for multi-image projective structure and motion. In: Buxton, B., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 709–720. Springer, Heidelberg (1996). doi:10.1007/3-540-61123-1_183

    Chapter  Google Scholar 

  3. Lowe, D.G.: Object recognition from local scale-invariant features. In: 7th IEEE International Conference on Computer Vision, pp. 1150–1157 (1999)

    Google Scholar 

  4. Bay, H., Tuytelaars, T., Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). doi:10.1007/11744023_32

    Chapter  Google Scholar 

  5. Hofer, M., Wendel, A., Bischof, H.: Line-based 3D reconstruction of wiry objects. In: 18th Computer Vision Winter Workshop (2013)

    Google Scholar 

  6. Hofer, M., Wendel, A., Bischof, H.: Incremental line-based 3D reconstruction using geometric constraints. In: 24th British Machine Vision Conference, pp. 92.1–92.11 (2013)

    Google Scholar 

  7. Jain, A., Kurz, C., Thormählen, T., Seidel, H.P.: Exploiting global connectivity constraints for reconstruction of 3D line segments from images. In: 2010 IEEE Computer Vision and Pattern Recognition, pp. 1586–1593 (2010)

    Google Scholar 

  8. Wang, Z., Wu, F., Hu, Z.: MSLD: a robust descriptor for line matching. Pattern Recogn. 42, 941–953 (2009)

    Article  Google Scholar 

  9. Hirose, K., Saito, H.: Fast line description for line-based slam. In: 23th British Machine Vision Conference, pp. 83.1–83.11 (2012)

    Google Scholar 

  10. Nakayama, Y., Honda, T., Saito, H., Shimizu, M., Yamaguchi, N.: Accurate camera pose estimation for KinectFusion based on line segment matching by LEHF. In: 22nd International Conference on Pattern Recognition. 2149–2154. (2014)

    Google Scholar 

  11. Nakayama, Y., Saito, H., Shimizu, M., Yamaguchi, N.: 3D line segment based model generation by RGB-D camera for camera pose estimation. In: Jawahar, C.V., Shan, S. (eds.) ACCV 2014. LNCS, vol. 9010, pp. 459–472. Springer, Heidelberg (2015). doi:10.1007/978-3-319-16634-6_34

    Google Scholar 

  12. Nakayama, Y., Saito, H., Shimizu, M., Yamaguchi, N.: Marker-less augmented reality framework using on-site 3D line-segment-based model generation. J. Imaging Sci. Technol. 60, 20401-1–20401-24 (2016)

    Article  Google Scholar 

  13. Hofer, M., Maurer, M., Bischof, H.: Improving sparse 3D models for man-made environments using line-based 3D reconstruction. In: 2014 2nd International Conference on 3D Vision, pp. 535–542 (2014)

    Google Scholar 

  14. Sugiura, T., Torii, A., Okutomi, M.: 3D surface reconstruction from point-and-line cloud. In: 3rd International Conference on 3D Vision, pp. 264–272 (2015)

    Google Scholar 

  15. Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohi, P., Shotton, J., Hodges, S., Fitzgibbon, A.: KinectFusion: real-time dense surface mapping and tracking. In: 2011 10th IEEE International Symposium on Mixed and Augmented Reality, pp. 127–136 (2011)

    Google Scholar 

  16. Yonezawa, T., Ueda, K.: Real-time 3D data reduction and reproduction of spatial model using line detection in RGB image. In: Joint 7th International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent Systems, pp. 727–730 (2014)

    Google Scholar 

  17. von Gioi, R.G., Jakubowicz, J., Morel, J.M., Randall, G.: LSD: a line segment detector. Image Process. On Line 2, 35–55 (2012)

    Article  Google Scholar 

  18. Nguyen, T., Reitmayr, G., Schmalstieg, D.: Structural modeling from depth images. IEEE Trans. Vis. Comput. Graph. 21, 1230–1240 (2015)

    Article  Google Scholar 

  19. Zhang, Z., Faugeras, O.: Building a 3D world model with a mobile robot: 3D line segment representation and integration. In: 10th International Conference on Pattern Recognition, pp. 38–42 (1990)

    Google Scholar 

  20. Holz, D., Behnke, S.: Fast range image segmentation and smoothing using approximate surface reconstruction and region growing. In: Lee, S., Cho, H., Yoon, K.-J., Lee, J. (eds.) Intelligent Autonomous Systems 12, vol. 194, pp. 61–73. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  21. Avery, B., Sandor, C., Thomas, B.H.: Improving spatial perception for augmented reality X-Ray vision. In: 2009 IEEE Virtual Reality Conference, pp. 79–82 (2009)

    Google Scholar 

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Acknowledgement

This work was partially supported by MEXT/JSPS Grant-in-Aid for Scientific Research(S) 24220004, and JST CREST “Intelligent Information Processing Systems Creating Co-Experience Knowledge and Wisdom with Human-Machine Harmonious Collaboration”.

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Authors and Affiliations

  1. Department of Information and Computer Science, Keio University, Yokohama, Japan

    Naoto Ienaga & Hideo Saito

Authors
  1. Naoto Ienaga
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  2. Hideo Saito
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Corresponding author

Correspondence to Naoto Ienaga .

Editor information

Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University, Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Ienaga, N., Saito, H. (2017). Reconstruction of 3D Models Consisting of Line Segments. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10117. Springer, Cham. https://doi.org/10.1007/978-3-319-54427-4_8

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  • DOI: https://doi.org/10.1007/978-3-319-54427-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54426-7

  • Online ISBN: 978-3-319-54427-4

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