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Learning Pairwise Inter-plane Relations for Piecewise Planar Reconstruction

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12352))

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Abstract

This paper proposes a novel single-image piecewise planar reconstruction technique that infers and enforces inter-plane relationships. Our approach takes a planar reconstruction result from an existing system, then utilizes convolutional neural network (CNN) to (1) classify if two planes are orthogonal or parallel; and 2) infer if two planes are touching and, if so, where in the image. We formulate an optimization problem to refine plane parameters and employ a message passing neural network to refine plane segmentation masks by enforcing the inter-plane relations. Our qualitative and quantitative evaluations demonstrate the effectiveness of the proposed approach in terms of plane parameters and segmentation accuracy.

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Notes

  1. 1.

    Ground-truth segmentation comes from plane-fitting to 3D points  [10]. For being conservative, they focus on high confidence areas with high point densities only, dropping the plane boundaries.

References

  1. Bauchet, J.P., Lafarge, F.: KIPPI: kinetic polygonal partitioning of images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3146–3154 (2018)

    Google Scholar 

  2. Chen, J., Liu, C., Wu, J., Furukawa, Y.: Floor-SP: inverse cad for floorplans by sequential room-wise shortest path. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2661–2670 (2019)

    Google Scholar 

  3. Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: ScanNet: richly-annotated 3d reconstructions of indoor scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5828–5839 (2017)

    Google Scholar 

  4. Deng, Z., Todorovic, S., Latecki, L.J.: Unsupervised object region proposals for RGB-D indoor scenes. Comput. Vis. Image Underst. 154, 127–136 (2017)

    Article  Google Scholar 

  5. Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Manhattan-world stereo. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1422–1429. IEEE (2009)

    Google Scholar 

  6. Gallup, D., Frahm, J.M., Pollefeys, M.: Piecewise planar and non-planar stereo for urban scene reconstruction. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1418–1425. IEEE (2010)

    Google Scholar 

  7. Hedau, V., Hoiem, D., Forsyth, D.: Recovering the spatial layout of cluttered rooms. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1849–1856. IEEE (2009)

    Google Scholar 

  8. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  9. Kushal, A., Seitz, S.M.: Single view reconstruction of piecewise swept surfaces. In: 2013 International Conference on 3D Vision-3DV 2013, pp. 239–246. IEEE (2013)

    Google Scholar 

  10. Liu, C., Kim, K., Gu, J., Furukawa, Y., Kautz, J.: PlanerCNN: 3D plane detection and reconstruction from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4450–4459 (2019)

    Google Scholar 

  11. Liu, C., Wu, J., Kohli, P., Furukawa, Y.: Raster-to-vector: revisiting floorplan transformation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2195–2203 (2017)

    Google Scholar 

  12. Liu, C., Yang, J., Ceylan, D., Yumer, E., Furukawa, Y.: PlaneNet: piece-wise planar reconstruction from a single RGB image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2579–2588 (2018)

    Google Scholar 

  13. Liu, C., Schwing, A.G., Kundu, K., Urtasun, R., Fidler, S.: Rent3D: floor-plan priors for monocular layout estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3413–3421 (2015)

    Google Scholar 

  14. Monszpart, A., Mellado, N., Brostow, G.J., Mitra, N.J.: RAPTER: rebuilding man-made scenes with regular arrangements of planes. ACM Trans. Graph. 34(4), 103–111 (2015)

    Article  Google Scholar 

  15. Nocedal, J., Wright, S.: Numerical Optimization. Springer, New York (2006). https://doi.org/10.1007/978-0-387-40065-5

    Book  MATH  Google Scholar 

  16. Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77(1–3), 157–173 (2008)

    Article  Google Scholar 

  17. Sharma, G., Goyal, R., Liu, D., Kalogerakis, E., Maji, S.: CSGNET: neural shape parser for constructive solid geometry. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5515–5523 (2018)

    Google Scholar 

  18. Shi, Y., Xu, K., Nießner, M., Rusinkiewicz, S., Funkhouser, T.: PlaneMatch: patch coplanarity prediction for robust RGB-D reconstruction. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 767–784. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01237-3_46

    Chapter  Google Scholar 

  19. 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 

  20. Sinha, S., Steedly, D., Szeliski, R.: Piecewise planar stereo for image-based rendering (2009)

    Google Scholar 

  21. Sun, C., Hsiao, C.W., Sun, M., Chen, H.T.: HorizonNet: learning room layout with 1D representation and pano stretch data augmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1047–1056 (2019)

    Google Scholar 

  22. Xiao, J., Furukawa, Y.: Reconstructing the world’s museums. Int. J. Comput. Vis. 110(3), 243–258 (2014)

    Article  Google Scholar 

  23. Yang, F., Zhou, Z.: Recovering 3D planes from a single image via convolutional neural networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 87–103. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01249-6_6

    Chapter  Google Scholar 

  24. Yu, Z., Zheng, J., Lian, D., Zhou, Z., Gao, S.: Single-image piece-wise planar 3D reconstruction via associative embedding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1029–1037 (2019)

    Google Scholar 

  25. Zebedin, L., Bauer, J., Karner, K., Bischof, H.: Fusion of Feature- and Area-Based Information for Urban Buildings Modeling from Aerial Imagery. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5305, pp. 873–886. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88693-8_64

    Chapter  Google Scholar 

  26. Zhang, F., Nauata, N., Furukawa, Y.: Conv-mpn: Convolutional message passing neural network for structured outdoor architecture reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  27. Zheng, J., Zhang, J., Li, J., Tang, R., Gao, S., Zhou, Z.: Structured3D: a large photo-realistic dataset for structured 3d modeling. arXiv preprint arXiv:1908.00222 (2019)

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Acknowledgments

This research is partially supported by NSERC Discovery Grants, NSERC Discovery Grants Accelerator Supplements, and DND/NSERC Discovery Grant Supplement.

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Authors and Affiliations

  1. Simon Fraser University, Burnaby, Canada

    Yiming Qian & Yasutaka Furukawa

Authors
  1. Yiming Qian
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  2. Yasutaka Furukawa
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Corresponding author

Correspondence to Yiming Qian .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Qian, Y., Furukawa, Y. (2020). Learning Pairwise Inter-plane Relations for Piecewise Planar Reconstruction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12352. Springer, Cham. https://doi.org/10.1007/978-3-030-58571-6_20

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  • DOI: https://doi.org/10.1007/978-3-030-58571-6_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58570-9

  • Online ISBN: 978-3-030-58571-6

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