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European Conference on Computer Vision

ECCV 2020: Computer Vision – ECCV 2020 pp 666–682Cite as

Joint 3D Layout and Depth Prediction from a Single Indoor Panorama Image

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12361))

Abstract

In this paper, we propose a method which jointly learns the layout prediction and depth estimation from a single indoor panorama image. Previous methods have considered layout prediction and depth estimation from a single panorama image separately. However, these two tasks are tightly intertwined. Leveraging the layout depth map as an intermediate representation, our proposed method outperforms existing methods for both panorama layout prediction and depth estimation. Experiments on the challenging real-world dataset of Stanford 2D–3D demonstrate that our approach obtains superior performance for both the layout prediction tasks (3D IoU: \(85.81\%\) v.s. \(79.79\%\)) and the depth estimation (Abs Rel: 0.068 v.s. 0.079).

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References

  1. Armeni, I., Sax, S., Zamir, A.R., Savarese, S.: Joint 2d–3d-semantic data for indoor scene understanding. arXiv preprint arXiv:1702.01105 (2017)

  2. Cao, Y., Wu, Z., Shen, C.: Estimating depth from monocular images as classification using deep fully convolutional residual networks. IEEE Trans. Circ. Syst. Video Technol. 28(11), 3174–3182 (2017)

    Article  Google Scholar 

  3. Cohen, T., Geiger, M., Köhler, J., Welling, M.: Convolutional networks for spherical signals. arXiv preprint arXiv:1709.04893 (2017)

  4. Cohen, T.S., Weiler, M., Kicanaoglu, B., Welling, M.: Gauge equivariant convolutional networks and the icosahedral cnn. arXiv preprint arXiv:1902.04615 (2019)

  5. Delage, E., Lee, H., Ng, A.Y.: A dynamic bayesian network model for autonomous 3d reconstruction from a single indoor image. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06). vol. 2, pp. 2418–2428. IEEE (2006)

    Google Scholar 

  6. Dhamo, H., Tateno, K., Laina, I., Navab, N., Tombari, F.: Peeking behind objects: layered depth prediction from a single image. Pattern Recogn. Lett. 125, 333–340 (2019)

    Article  Google Scholar 

  7. Eder, M., Moulon, P., Guan, L.: Pano popups: indoor 3d reconstruction with a plane-aware network. In: 2019 International Conference on 3D Vision (3DV), pp. 76–84. IEEE (2019)

    Google Scholar 

  8. Eder, M., Price, T., Vu, T., Bapat, A., Frahm, J.M.: Mapped convolutions. arXiv preprint arXiv:1906.11096 (2019)

  9. Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. In: Advances in Neural Information Processing Systems, pp. 2366–2374 (2014)

    Google Scholar 

  10. 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 

  11. Hu, J., Ozay, M., Zhang, Y., Okatani, T.: Revisiting single image depth estimation: toward higher resolution maps with accurate object boundaries. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1043–1051. IEEE (2019)

    Google Scholar 

  12. Izadinia, H., Shan, Q., Seitz, S.M.: Im2cad. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5134–5143 (2017)

    Google Scholar 

  13. Jiang, C., Huang, J., Kashinath, K., Marcus, P., Niessner, M., et al.: Spherical cnns on unstructured grids. arXiv preprint arXiv:1901.02039 (2019)

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

  15. Laina, I., Rupprecht, C., Belagiannis, V., Tombari, F., Navab, N.: Deeper depth prediction with fully convolutional residual networks. In: 3D Vision (3DV), 2016 Fourth International Conference on, pp. 239–248. IEEE (2016)

    Google Scholar 

  16. Lee, C.Y., Badrinarayanan, V., Malisiewicz, T., Rabinovich, A.: Roomnet: end-to-end room layout estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4865–4874 (2017)

    Google Scholar 

  17. Li, B., Shen, C., Dai, Y., Van Den Hengel, A., He, M.: Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1119–1127 (2015)

    Google Scholar 

  18. Liu, C., Kohli, P., Furukawa, Y.: Layered scene decomposition via the occlusion-crf. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 165–173 (2016)

    Google Scholar 

  19. 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 

  20. Mallya, A., Lazebnik, S.: Learning informative edge maps for indoor scene layout prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 936–944 (2015)

    Google Scholar 

  21. Micusik, B., Kosecka, J.: Piecewise planar city 3d modeling from street view panoramic sequences. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2906–2912. IEEE (2009)

    Google Scholar 

  22. Zama Ramirez, P., Poggi, M., Tosi, F., Mattoccia, S., Di Stefano, Luigi: Geometry meets semantics for semi-supervised monocular depth estimation. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 298–313. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20893-6_19

    Chapter  Google Scholar 

  23. Ren, Y., Li, S., Chen, C., Kuo, C.C.J.: A coarse-to-fine indoor layout estimation (cfile) method. In: Asian Conference on Computer Vision, pp. 36–51. Springer (2016)

    Google Scholar 

  24. Su, Y.C., Grauman, K.: Learning spherical convolution for fast features from 360 imagery. In: Advances in Neural Information Processing Systems, pp. 529–539 (2017)

    Google Scholar 

  25. 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 

  26. Tateno, K., Navab, N., Tombari, F.: Distortion-aware convolutional filters for dense prediction in panoramic images. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 707–722 (2018)

    Google Scholar 

  27. Tulsiani, S., Tucker, R., Snavely, N.: Layer-structured 3d scene inference via view synthesis. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 302–317 (2018)

    Google Scholar 

  28. Von Gioi, R.G., Jakubowicz, J., Morel, J.M., Randall, G.: Lsd: a fast line segment detector with a false detection control. IEEE Trans. Pattern Anal. Mach. Intell. 32(4), 722–732 (2008)

    Article  Google Scholar 

  29. Wang, X., Fouhey, D., Gupta, A.: Designing deep networks for surface normal estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 539–547 (2015)

    Google Scholar 

  30. Yang, S.T., Wang, F.E., Peng, C.H., Wonka, P., Sun, M., Chu, H.K.: Dula-net: a dual-projection network for estimating room layouts from a single rgb panorama. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3363–3372 (2019)

    Google Scholar 

  31. Yin, W., Liu, Y., Shen, C., Yan, Y.: Enforcing geometric constraints of virtual normal for depth prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5684–5693 (2019)

    Google Scholar 

  32. Zhang, J., Kan, C., Schwing, A.G., Urtasun, R.: Estimating the 3d layout of indoor scenes and its clutter from depth sensors. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1273–1280 (2013)

    Google Scholar 

  33. Zhang, Y., Song, S., Tan, P., Xiao, J.: Panocontext: a whole-room 3d context model for panoramic scene understanding. In: European Conference on Computer Vision, pp. 668–686. Springer (2014)

    Google Scholar 

  34. Zioulis, N., Karakottas, A., Zarpalas, D., Daras, P.: Omnidepth: dense depth estimation for indoors spherical panoramas. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 448–465 (2018)

    Google Scholar 

  35. Zou, C., Colburn, A., Shan, Q., Hoiem, D.: Layoutnet: reconstructing the 3d room layout from a single rgb image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2051–2059 (2018)

    Google Scholar 

  36. Zou, C., Guo, R., Li, Z., Hoiem, D.: Complete 3d scene parsing from an rgbd image. Int. J. Comput. Vis. 127(2), 143–162 (2019)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Vision Laboratory, University of Amsterdam, Amsterdam, The Netherlands

    Wei Zeng, Sezer Karaoglu & Theo Gevers

  2. 3DUniversum, Science Park 400, Amsterdam, The Netherlands

    Sezer Karaoglu & Theo Gevers

Authors
  1. Wei Zeng
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  2. Sezer Karaoglu
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  3. Theo Gevers
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Corresponding author

Correspondence to Wei Zeng .

Editor information

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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Zeng, W., Karaoglu, S., Gevers, T. (2020). Joint 3D Layout and Depth Prediction from a Single Indoor Panorama Image. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12361. Springer, Cham. https://doi.org/10.1007/978-3-030-58517-4_39

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  • DOI: https://doi.org/10.1007/978-3-030-58517-4_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58516-7

  • Online ISBN: 978-3-030-58517-4

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