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Visual Graphs from Motion (VGfM): Scene Understanding with Object Geometry Reasoning

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Computer Vision – ACCV 2018 (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11363))

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Abstract

Recent approaches on visual scene understanding attempt to build a scene graph – a computational representation of objects and their pairwise relationships. Such rich semantic representation is very appealing, yet difficult to obtain from a single image, especially when considering complex spatial arrangements in the scene. Differently, an image sequence conveys useful information using the multi-view geometric relations arising from camera motions. Indeed, object relationships are naturally related to the 3D scene structure. To this end, this paper proposes a system that first computes the geometrical location of objects in a generic scene and then efficiently constructs scene graphs from video by embedding such geometrical reasoning. Such compelling representation is obtained using a new model where geometric and visual features are merged using an RNN framework. We report results on a dataset we created for the task of 3D scene graph generation in multiple views.

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Notes

  1. 1.

    Code and data can be found at: https://github.com/paulgay/VGfM.

  2. 2.

    The supplemental material provides more mathematical details about this step.

  3. 3.

    We refer to supplemental material for further mathematical details.

References

  1. Bao, S.Y., Bagra, M., Chao, Y.W., Savarese, S.: Semantic structure from motion with points, regions, and objects. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2703–2710. IEEE (2012)

    Google Scholar 

  2. Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3D object detection network for autonomous driving. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 211–219. IEEE (2017)

    Google Scholar 

  3. Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)

  4. Choy, C.B., Xu, D., Gwak, J.Y., Chen, K., Savarese, S.: 3D-R2N2: a unified approach for single and multi-view 3D object reconstruction. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 628–644. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_38

    Chapter  Google Scholar 

  5. Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: Scannet: Richly-annotated 3D reconstructions of indoor scenes. In: Computer Vision and Pattern Recognition (CVPR), pp. 2075–2084. IEEE (2017)

    Google Scholar 

  6. Dai, A., Nießner, M., Zollöfer, M., Izadi, S., Theobalt, C.: BundleFusion: real-time globally consistent 3D reconstruction using on-the-fly surface re-integration. Trans. Graph. (TOG) 36, 76a (2017)

    Article  Google Scholar 

  7. Dai, B., Zhang, Y., Lin, D.: Detecting visual relationships with deep relational networks. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3298–3308 (2017)

    Google Scholar 

  8. Daniels, Z.A., Metaxas, D.N.: Scenarios: a new representation for complex scene understanding. arXiv preprint arXiv:1802.06117 (2018)

  9. Desai, C., Ramanan, D., Fowlkes, C.: Discriminative models for static human-object interactions. In: Computer Vision and Pattern Recognition Workshops (CVPR), pp. 9–16. IEEE (2010)

    Google Scholar 

  10. Dong, J., Fei, X., Soatto, S.: Visual inertial semantic scene representation for 3D object detection. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 782–790. IEEE (2017)

    Google Scholar 

  11. Gay, P., Rubino, C., Bansal, V., Del Bue, A.: Probabilistic structure from motion with objects (PSfMO). In: International Conference on Computer Vision (ICCV), pp. 3075–3084. IEEE (2017)

    Google Scholar 

  12. Hane, C., Zach, C., Cohen, A., Pollefeys, M.: Dense semantic 3D reconstruction. Pattern Anal. Mach. Intell. (PAMI) 39(9), 1730–1743 (2017)

    Article  Google Scholar 

  13. Johnson, J., et al.: Image retrieval using scene graphs. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3668–3678. IEEE (2015)

    Google Scholar 

  14. Kang, K., Ouyang, W., Li, H., Wang, X.: Object detection from video tubelets with convolutional neural networks. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1744–1756. IEEE (2016)

    Google Scholar 

  15. Krishna, R., et al.: Visual genome: connecting language and vision using crowdsourced dense image annotations. Int. J. Comput. Vis. (IJCV) 123(1), 32–73 (2017)

    Article  MathSciNet  Google Scholar 

  16. Li, Y., Ouyang, W., Zhou, B., Wang, K., Wang, X.: Scene graph generation from objects, phrases and region captions. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1261–1270. IEEE (2017)

    Google Scholar 

  17. Liang, X., Shen, X., Feng, J., Lin, L., Yan, S.: Semantic object parsing with graph LSTM. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 125–143. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_8

    Chapter  Google Scholar 

  18. Liao, W., Shuai, L., Rosenhahn, B., Yang, M.Y.: Natural language guided visual relationship detection. arXiv preprint arXiv:1711.06032 (2017)

  19. Lu, C., Krishna, R., Bernstein, M., Fei-Fei, L.: Visual relationship detection with language priors. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 852–869. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_51

    Chapter  Google Scholar 

  20. Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Rob. 31(5), 1147–1163 (2015)

    Article  Google Scholar 

  21. Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: Computer Vision and Pattern Recognition (CVPR), pp. 1544–1556. IEEE (2015)

    Google Scholar 

  22. Peyre, J., Laptev, I., Schmid, C., Sivic, J.: Weakly-supervised learning of visual relations. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 5189–5198 (2017). https://doi.org/10.1109/ICCV.2017.554. ISSN 2380-7504

  23. Reddy, N.D., Singhal, P., Chari, V., Krishna, K.M.: Dynamic body VSLAM with semantic constraints. In: International Conference on Intelligent Robots (ICIR), pp. 1897–1904. IEEE (2015)

    Google Scholar 

  24. Rubino, C., Crocco, M., Del Bue, A.: 3D object localisation from multi-view image detections. Pattern Anal. Mach. Intell. (PAMI) 40(6), 1281–1294 (2018)

    Google Scholar 

  25. Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Neural Information Processing Systems NIPS, pp. 3856–3866 (2017)

    Google Scholar 

  26. Sengupta, S., Greveson, E., Shahrokni, A., Torr, P.H.S.: Urban 3D semantic modelling using stereo vision. In: International Conference on Robotics and Automation, pp. 580–585. IEEE (2013)

    Google Scholar 

  27. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  28. Sung, M., Kim, V.G., Angst, R., Guibas, L.: Data-driven structural priors for shape completion. ACM Trans. Graph. (TOG) 34(6), 175 (2015)

    Article  Google Scholar 

  29. Tripathi, S., Lipton, Z.C., Belongie, S.J., Nguyen, T.Q.: Context matters: refining object detection in video with recurrent neural networks. In: British Machine Vision Conference (BMVC), pp. 1723–1731. BMVA (2016)

    Google Scholar 

  30. Tulsiani, S., Gupta, S., Fouhey, D., Efros, A.A., Malik, J.: Factoring shape, pose, and layout from the 2D image of a 3D scene. In: Computer Vision and Pattern Regognition (CVPR). IEEE (2018)

    Google Scholar 

  31. Xu, D., Zhu, Y., Choy, C.B., Fei-Fei, L.: Scene graph generation by iterative message passing. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3097–3106. IEEE (2017)

    Google Scholar 

  32. Zellers, R., Yatskar, M., Thomson, S., Choi, Y.: Neural motifs: scene graph parsing with global context. In: Conference on Computer Vision and Pattern Recognition CVPR, pp. 3294–3304. IEEE (2018)

    Google Scholar 

  33. Zhang, H., Kyaw, Z., Chang, S.F., Chua, T.S.: Visual translation embedding network for visual relation detection. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4–12. IEEE (2017)

    Google Scholar 

  34. Zheng, S., et al.: Conditional random fields as recurrent neural networks. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1529–1537 (2015). https://doi.org/10.1109/ICCV.2015.179. ISSN 2380-7504

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Author information

Authors and Affiliations

  1. Visual Geometry and Modelling (VGM) Lab, Istituto Italiano di Tecnologia (IIT), Via Morego 30, 16163, Genova, Italy

    Paul Gay & Alessio Del Bue

  2. Center for Cultural Heritage Technology, Istituto Italiano di Tecnologia (IIT), Via Morego 30, 16163, Genova, Italy

    James Stuart

Authors
  1. Paul Gay
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  2. James Stuart
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  3. Alessio Del Bue
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Corresponding author

Correspondence to Paul Gay .

Editor information

Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C. V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

1 Electronic supplementary material

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Supplementary material 1 (pdf 237 KB)

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Gay, P., Stuart, J., Del Bue, A. (2019). Visual Graphs from Motion (VGfM): Scene Understanding with Object Geometry Reasoning. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11363. Springer, Cham. https://doi.org/10.1007/978-3-030-20893-6_21

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  • DOI: https://doi.org/10.1007/978-3-030-20893-6_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20892-9

  • Online ISBN: 978-3-030-20893-6

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