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3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Significant geometric structures can be compactly described by global wireframes in the estimation of 3D room layout from a single panoramic image. Based on this observation, we present an alternative approach to estimate the walls in 3D space by modeling long-range geometric patterns in a learnable Hough Transform block. We transform the image feature from a cubemap tile to the Hough space of a Manhattan world and directly map the feature to the geometric output. The convolutional layers not only learn the local gradient-like line features, but also utilize the global information to successfully predict occluded walls with a simple network structure. Unlike most previous work, the predictions are performed individually on each cubemap tile, and then assembled to get the layout estimation. Experimental results show that we achieve comparable results with recent state-of-the-art in prediction accuracy and performance. Code is available at https://github.com/Starrah/DMH-Net.

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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. Ballard, D.H.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recogn. 13(2), 111–122 (1981)

    Article  Google Scholar 

  3. Beltrametti, M.C., Campi, C., Massone, A.M., Torrente, M.: Geometry of the Hough transforms with applications to synthetic data. Math. Comput. Sci. 1–23 (2020)

    Google Scholar 

  4. Bertamini, M., Helmy, M., Bates, D.: The visual system prioritizes locations near corners of surfaces (not just locations near a corner). Attention Percept. Psychophys. 75(8), 1748–1760 (2013). https://doi.org/10.3758/s13414-013-0514-1

    Article  Google Scholar 

  5. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)

    Article  Google Scholar 

  6. Chang, A., et al.: Matterport3D: learning from RGB-D data in indoor environments. In: Proceedings of the International Conference on 3D Vision (3DV), pp. 667–676 (2017)

    Google Scholar 

  7. Coughlan, J.M., Yuille, A.L.: Manhattan world: compass direction from a single image by Bayesian inference. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 941–947 (1999)

    Google Scholar 

  8. Dasgupta, S., Fang, K., Chen, K., Savarese, S.: Delay: robust spatial layout estimation for cluttered indoor scenes. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 616–624 (2016)

    Google Scholar 

  9. Del Pero, L., Bowdish, J., Kermgard, B., Hartley, E., Barnard, K.: Understanding Bayesian rooms using composite 3D object models. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 153–160 (2013)

    Google Scholar 

  10. Delage, E., Lee, H., Ng, A.Y.: A dynamic Bayesian network model for autonomous 3D reconstruction from a single indoor image. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2418–2428 (2006)

    Google Scholar 

  11. Fernandez-Labrador, C., Facil, J.M., Perez-Yus, A., Demonceaux, C., Civera, J., Guerrero, J.J.: Corners for layout: End-to-end layout recovery from 360 images. IEEE Robot. Autom. Lett. (RA-L) 5(2), 1255–1262 (2020)

    Google Scholar 

  12. Greene, N.: Environment mapping and other applications of world projections. IEEE Comput. Graph. Appl. 6(11), 21–29 (1986)

    Article  Google Scholar 

  13. Gupta, A., Hebert, M., Kanade, T., Blei, D.: Estimating spatial layout of rooms using volumetric reasoning about objects and surfaces. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 1288–1296 (2010)

    Google Scholar 

  14. Han, Q., Zhao, K., Xu, J., Cheng, M.M.: Deep Hough transform for semantic line detection. IEEE Trans. Pattern Anal. Mach. Intell. 249–265 (2021)

    Google Scholar 

  15. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)

    Google Scholar 

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

    Google Scholar 

  17. Horry, Y., Anjyo, K.I., Arai, K.: Tour into the picture: using a spidery mesh interface to make animation from a single image. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, pp. 225–232 (1997)

    Google Scholar 

  18. Hough, P.V.: Method and means for recognizing complex patterns, US Patent 3,069,654, 18 December 1962

    Google Scholar 

  19. Izadinia, H., Shan, Q., Seitz, S.M.: IM2CAD. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2422–2431 (2017)

    Google Scholar 

  20. Jiang, Z., Xiang, Z., Xu, J., Zhao, M.: LGT-Net: indoor panoramic room layout estimation with geometry-aware transformer network. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1654–1663 (2022)

    Google Scholar 

  21. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the International Conference on Learning Representations (ICLR) (2015)

    Google Scholar 

  22. 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 (ICCV), pp. 4875–4884 (2017)

    Google Scholar 

  23. Lee, D.C., Hebert, M., Kanade, T.: Geometric reasoning for single image structure recovery. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2136–2143 (2009)

    Google Scholar 

  24. Lin, Y., Pintea, S.L., van Gemert, J.C.: Deep Hough-transform line priors. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12367, pp. 323–340. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58542-6_20

    Chapter  Google Scholar 

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

    Google Scholar 

  26. Pintore, G., Agus, M., Gobbetti, E.: AtlantaNet: inferring the 3D indoor layout from a single \(360^\circ \) image beyond the Manhattan world assumption. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 432–448. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58598-3_26

    Chapter  Google Scholar 

  27. Pintore, G., Garro, V., Ganovelli, F., Gobbetti, E., Agus, M.: Omnidirectional image capture on mobile devices for fast automatic generation of 2.5 d indoor maps. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–9 (2016)

    Google Scholar 

  28. Qi, C.R., Litany, O., He, K., Guibas, L.J.: Deep Hough voting for 3D object detection in point clouds. In: Proceedings of the IEEE International Confernece on Computer Vision (ICCV), pp. 9276–9285 (2019)

    Google Scholar 

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

    Google Scholar 

  30. Schwing, A.G., Hazan, T., Pollefeys, M., Urtasun, R.: Efficient structured prediction for 3d indoor scene understanding. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2815–2822 (2012)

    Google Scholar 

  31. Schwing, A.G., Urtasun, R.: Efficient exact inference for 3D indoor scene understanding. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 299–313. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33783-3_22

    Chapter  Google Scholar 

  32. Sobel, I., Feldman, G.: A 3x3 isotropic gradient operator for image processing. A talk at the Stanford Artificial Project, pp. 271–272 (1968)

    Google Scholar 

  33. 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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1047–1056 (2019)

    Google Scholar 

  34. Sun, C., Sun, M., Chen, H.T.: Hohonet: 360 indoor holistic understanding with latent horizontal features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2573–2582 (2021)

    Google Scholar 

  35. Wang, F.E., Yeh, Y.H., Sun, M., Chiu, W.C., Tsai, Y.H.: LED2-Net: monocular 360deg layout estimation via differentiable depth rendering. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12956–12965 (2021)

    Google Scholar 

  36. Xu, J., Stenger, B., Kerola, T., Tung, T.: Pano2CAD: room layout from a single panorama image. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 354–362 (2017)

    Google Scholar 

  37. Yang, C., Zheng, J., Dai, X., Tang, R., Ma, Y., Yuan, X.: Learning to reconstruct 3D non-cuboid room layout from a single RGB image. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2534–2543 (2022)

    Google Scholar 

  38. Yang, H., Zhang, H.: Efficient 3D room shape recovery from a single panorama. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5422–5430 (2016)

    Google Scholar 

  39. 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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3363–3372 (2019)

    Google Scholar 

  40. Yang, Y., Jin, S., Liu, R., Bing Kang, S., Yu, J.: Automatic 3d indoor scene modeling from single panorama. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3926–3934 (2018)

    Google Scholar 

  41. Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 636–644 (2017)

    Google Scholar 

  42. Zeng, W., Karaoglu, S., Gevers, T.: Joint 3D layout and depth prediction from a single indoor panorama image. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 666–682. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58517-4_39

    Chapter  Google Scholar 

  43. Zhang, Y., Song, S., Tan, P., Xiao, J.: PanoContext: a whole-room 3D context model for panoramic scene understanding. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 668–686. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10599-4_43

    Chapter  Google Scholar 

  44. Zhao, H., Lu, M., Yao, A., Guo, Y., Chen, Y., Zhang, L.: Physics inspired optimization on semantic transfer features: an alternative method for room layout estimation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 870–878 (2017)

    Google Scholar 

  45. Zhao, Y., Zhu, S.C.: Scene parsing by integrating function, geometry and appearance models. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3119–3126 (2013)

    Google Scholar 

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

    Google Scholar 

  47. Zou, C., et al.: Manhattan room layout reconstruction from a single \(360^{\circ }\) image: a comparative study of state-of-the-art methods. Int. J. Comput. Vision (IJCV) 129(5), 1410–1431 (2021)

    Article  Google Scholar 

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Acknowledgements

This work was supported by National Natural Science Funds of China (No. 62088102, 62021002) and ByteDance Research Collaboration Project.

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Authors and Affiliations

  1. BNRist, THUIBCS, BLBCI, KLISS, School of Software, Tsinghua University, Beijing, China

    Yining Zhao & Yue Gao

  2. Pico IDL, ByteDance, Beijing, China

    Chao Wen & Zhou Xue

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  1. Yining Zhao
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  2. Chao Wen
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  3. Zhou Xue
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  4. Yue Gao
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Correspondence to Yue Gao .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Zhao, Y., Wen, C., Xue, Z., Gao, Y. (2022). 3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13661. Springer, Cham. https://doi.org/10.1007/978-3-031-19769-7_37

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  • DOI: https://doi.org/10.1007/978-3-031-19769-7_37

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