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Learning Object Depth from Camera Motion and Video Object Segmentation

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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

Video object segmentation, i.e., the separation of a target object from background in video, has made significant progress on real and challenging videos in recent years. To leverage this progress in 3D applications, this paper addresses the problem of learning to estimate the depth of segmented objects given some measurement of camera motion (e.g., from robot kinematics or vehicle odometry). We achieve this by, first, introducing a diverse, extensible dataset and, second, designing a novel deep network that estimates the depth of objects using only segmentation masks and uncalibrated camera movement. Our data-generation framework creates artificial object segmentations that are scaled for changes in distance between the camera and object, and our network learns to estimate object depth even with segmentation errors. We demonstrate our approach across domains using a robot camera to locate objects from the YCB dataset and a vehicle camera to locate obstacles while driving.

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Notes

  1. 1.

    Dataset and source code website: https://github.com/griffbr/ODMS.

References

  1. Bao, L., Wu, B., Liu, W.: CNN in MRF: video object segmentation via inference in A CNN-based higher-order spatio-temporal MRF. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Google Scholar 

  2. Caelles, S., Maninis, K.K., Pont-Tuset, J., Leal-Taixé, L., Cremers, D., Van Gool, L.: One-shot video object segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  3. Caelles, S., et al.: The 2018 DAVIS challenge on video object segmentation. CoRR abs/1803.00557 (2018)

    Google Scholar 

  4. Calli, B., Walsman, A., Singh, A., Srinivasa, S., Abbeel, P., Dollar, A.M.: Benchmarking in manipulation research: using the Yale-CMU-Berkeley object and model set. IEEE Robot. Autom. Mag. 22(3), 36–52 (2015)

    Article  Google Scholar 

  5. Chen, D.J., Chen, H.T., Chang, L.W.: Video object co-segmentation. In: ACM International Conference on Multimedia (2012)

    Google Scholar 

  6. Chen, Y., Pont-Tuset, J., Montes, A., Van Gool, L.: Blazingly fast video object segmentation with pixel-wise metric learning. In: Computer Vision and Pattern Recognition (CVPR) (2018)

    Google Scholar 

  7. Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

  8. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009)

    Google Scholar 

  9. Faktor, A., Irani, M.: Video segmentation by non-local consensus voting. In: British Machine Vision Conference (BMVC) (2014)

    Google Scholar 

  10. Ferguson, M., Law, K.: A 2D–3D object detection system for updating building information models with mobile robots. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (2019)

    Google Scholar 

  11. Florence, V., Corso, J.J., Griffin, B.: Robot-supervised learning for object segmentation. In: The IEEE International Conference on Robotics and Automation (ICRA) (2020)

    Google Scholar 

  12. Gan, L., Zhang, R., Grizzle, J.W., Eustice, R.M., Ghaffari, M.: Bayesian spatial kernel smoothing for scalable dense semantic mapping. IEEE Robot. Autom. Lett. (RA-L) 5(2), 790–797 (2020)

    Google Scholar 

  13. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The Kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012)

    Google Scholar 

  14. Griffin, B., Florence, V., Corso, J.J.: Video object segmentation-based visual servo control and object depth estimation on a mobile robot. In: IEEE Winter Conference on Applications of Computer Vision (WACV) (2020)

    Google Scholar 

  15. Griffin, B.A., Corso, J.J.: BubbleNets: learning to select the guidance frame in video object segmentation by deep sorting frames. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  16. Griffin, B.A., Corso, J.J.: Tukey-inspired video object segmentation. In: IEEE Winter Conference on Applications of Computer Vision (WACV) (2019)

    Google Scholar 

  17. He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: The IEEE International Conference on Computer Vision (ICCV) (2017)

    Google Scholar 

  18. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

  19. Ittelson, W.H.: Size as a cue to distance: radial motion. Am. J. Psychol. 64(2), 188–202 (1951)

    Article  Google Scholar 

  20. Kasten, Y., Galun, M., Basri, R.: Resultant based incremental recovery of camera pose from pairwise matches. In: IEEE Winter Conference on Applications of Computer Vision (WACV) (2019)

    Google Scholar 

  21. Khan, S.H., Guo, Y., Hayat, M., Barnes, N.: Unsupervised primitive discovery for improved 3d generative modeling. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  22. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2014)

    Google Scholar 

  23. Kumawat, S., Raman, S.: LP-3DCNN: unveiling local phase in 3D convolutional neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  24. Lee, Y.J., Kim, J., Grauman, K.: Key-segments for video object segmentation. In: IEEE International Conference on Computer Vision (ICCV) (2011)

    Google Scholar 

  25. Li, F., Kim, T., Humayun, A., Tsai, D., Rehg, J.M.: Video segmentation by tracking many figure-ground segments. In: The IEEE International Conference on Computer Vision (ICCV) (2013)

    Google Scholar 

  26. Li, Z., et al.: Learning the depths of moving people by watching frozen people. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  27. Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  28. Liu, B., He, X.: Multiclass semantic video segmentation with object-level active inference. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)

    Google Scholar 

  29. Liu, R., et al.: An intriguing failing of convolutional neural networks and the coordConv solution. In: Advances in Neural Information Processing Systems, vol. 31 (NIPS)

    Google Scholar 

  30. Liu, Y., Fan, B., Xiang, S., Pan, C.: Relation-shape convolutional neural network for point cloud analysis. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  31. Lu, J., Xu, R., Corso, J.J.: Human action segmentation with hierarchical supervoxel consistency. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)

    Google Scholar 

  32. Luiten, J., Voigtlaender, P., Leibe, B.: Premvos: Proposal-generation, refinement and merging for video object segmentation. In: Asian Conference on Computer Vision (ACCV) (2018)

    Google Scholar 

  33. Maninis, K., et al.: Video object segmentation without temporal information. IEEE Trans. Pattern Anal. Mach. Intell. 41, 1515–1530 (2018)

    Article  Google Scholar 

  34. Mur-Artal, R., Tards, J.D.: ORB-SLAM2: an open-source slam system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. (T-RO) 33, 1255-1262 (2017)

    Google Scholar 

  35. Oh, S.W., Lee, J.Y., Sunkavalli, K., Kim, S.J.: Fast video object segmentation by reference-guided mask propagation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Google Scholar 

  36. Oneata, D., Revaud, J., Verbeek, J., Schmid, C.: Spatio-temporal object detection proposals. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 737–752. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_48

    Chapter  Google Scholar 

  37. Papazoglou, A., Ferrari, V.: Fast object segmentation in unconstrained video. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2013)

    Google Scholar 

  38. Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

  39. Pont-Tuset, J., Perazzi, F., Caelles, S., Arbelaez, P., Sorkine-Hornung, A., Gool, L.V.: The 2017 DAVIS challenge on video object segmentation. CoRR abs/1704.00675 (2017)

    Google Scholar 

  40. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  41. Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.M.: The SYNTHIA dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

  42. Schonberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

  43. Soomro, K., Idrees, H., Shah, M.: Action localization in videos through context walk. In: IEEE International Conference on Computer Vision (ICCV) (2015)

    Google Scholar 

  44. Soomro, K., Idrees, H., Shah, M.: Predicting the where and what of actors and actions through online action localization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

  45. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: DropOut: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  46. Swanston, M.T., Gogel, W.C.: Perceived size and motion in depth from optical expansion. Percept. Psychophys. 39, 309–326 (1986)

    Article  Google Scholar 

  47. Tang, K., Sukthankar, R., Yagnik, J., Fei-Fei, L.: Discriminative segment annotation in weakly labeled video. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013)

    Google Scholar 

  48. Tighe, J., Lazebnik, S.: SuperParsing: scalable nonparametric image parsing with superpixels. Int. J. Comput. Vis. 101(2), 352–365 (2012)

    MathSciNet  Google Scholar 

  49. Tsai, D., Flagg, M., Nakazawa, A., Rehg, J.M.: Motion coherent tracking using multi-label MRF optimization. Int. J. Comput. Vis. 100(2), 190–202 (2012)

    Article  MathSciNet  Google Scholar 

  50. Vijayanarasimhan, S., Grauman, K.: What’s it going to cost you?: Predicting effort vs. informativeness for multi-label image annotations. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009)

    Google Scholar 

  51. Voigtlaender, P., Leibe, B.: Online adaptation of convolutional neural networks for video object segmentation. In: British Machine Vision Conference (BMVC) (2017)

    Google Scholar 

  52. Wang, C., et al.: DenseFusion: 6D object pose estimation by iterative dense fusion. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  53. Wehrwein, S., Szeliski, R.: Video segmentation with background motion models. In: British Machine Vision Conference (BMVC) (2017)

    Google Scholar 

  54. Xu, C., Corso, J.J.: LIBSVX: a supervoxel library and benchmark for early video processing. Int. J. Comput. Vis. 119(3), 272–290 (2016)

    Article  MathSciNet  Google Scholar 

  55. Xu, N., Yang, L., Fan, Y., Yue, D., Liang, Y., Yang, J., Huang, T.S.: Youtube-VOS: a large-scale video object segmentation benchmark. CoRR abs/1809.03327 (2018)

    Google Scholar 

  56. Yamaguchi, U., Saito, F., Ikeda, K., Yamamoto, T.: HSR, human support robot as research and development platform. The Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2015, vol. 6, pp. 39–40 (2015)

    Google Scholar 

  57. Yamamoto, T., Terada, K., Ochiai, A., Saito, F., Asahara, Y., Murase, K.: Development of human support robot as the research platform of a domestic mobile manipulator. ROBOMECH J. 6(1), 4 (2019)

    Article  Google Scholar 

  58. Yang, L., Wang, Y., Xiong, X., Yang, J., Katsaggelos, A.K.: Efficient video object segmentation via network modulation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Google Scholar 

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Acknowledgements

We thank Madan Ravi Ganesh, Parker Koch, and Luowei Zhou for various discussions throughout this work. Toyota Research Institute (“TRI”) provided funds to assist the authors with their research but this article solely reflects the opinions and conclusions of its authors and not TRI or any other Toyota entity.

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Authors and Affiliations

  1. University of Michigan, Ann Arbor, USA

    Brent A. Griffin & Jason J. Corso

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  1. Brent A. Griffin
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Correspondence to Brent A. Griffin .

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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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Griffin, B.A., Corso, J.J. (2020). Learning Object Depth from Camera Motion and Video Object Segmentation. 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_18

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  • DOI: https://doi.org/10.1007/978-3-030-58571-6_18

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