Advertisement

Consistency Guided Scene Flow Estimation

Conference paper
  • 694 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12352)

Abstract

Consistency Guided Scene Flow Estimation (CGSF) is a self-supervised framework for the joint reconstruction of 3D scene structure and motion from stereo video. The model takes two temporal stereo pairs as input, and predicts disparity and scene flow. The model self-adapts at test time by iteratively refining its predictions. The refinement process is guided by a consistency loss, which combines stereo and temporal photo-consistency with a geometric term that couples disparity and 3D motion. To handle inherent modeling error in the consistency loss (e.g. Lambertian assumptions) and for better generalization, we further introduce a learned, output refinement network, which takes the initial predictions, the loss, and the gradient as input, and efficiently predicts a correlated output update. In multiple experiments, including ablation studies, we show that the proposed model can reliably predict disparity and scene flow in challenging imagery, achieves better generalization than the state-of-the-art, and adapts quickly and robustly to unseen domains.

Keywords

Scene flow Disparity estimation Stereo video Geometric constraints Self-supervised learning 

Supplementary material

504444_1_En_8_MOESM1_ESM.pdf (2.1 mb)
Supplementary material 1 (pdf 2140 KB)

References

  1. 1.
    Aleotti, F., Poggi, M., Tosi, F., Mattoccia, S.: Learning end-to-end scene flow by distilling single tasks knowledge. In: AAAI (2020)Google Scholar
  2. 2.
    Andrychowicz, M., et al.: Learning to learn by gradient descent by gradient descent. In: Advances in Neural Information Processing Systems, pp. 3981–3989 (2016)Google Scholar
  3. 3.
    Basha, T., Moses, Y., Kiryati, N.: Multi-view scene flow estimation: a view centered variational approach. Int. J. Comput. Vis. 101(1), 6–21 (2013)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Casser, V., Pirk, S., Mahjourian, R., Angelova, A.: Depth prediction without the sensors: leveraging structure for unsupervised learning from monocular videos. In: AAAI (2019)Google Scholar
  5. 5.
    Chang, J., Chen, Y.: Pyramid stereo matching network. In: CVPR (2018)Google Scholar
  6. 6.
    Chen, Y., Schmid, C., Sminchisescu, C.: Self-supervised learning with geometric constraints in monocular video: connecting flow, depth, and camera. In: ICCV (2019)Google Scholar
  7. 7.
    Clark, R., Bloesch, M., Czarnowski, J., Leutenegger, S., Davison, A.J.: LS-Net: Learning to solve nonlinear least squares for monocular stereo. arXiv preprint arXiv:1809.02966 (2018)
  8. 8.
    Dosovitskiy, A., et al.: FlowNet: learning optical flow with convolutional networks. In: ICCV (2015)Google Scholar
  9. 9.
    Godard, C., Mac Aodha, O., Brostow, G.J.: Unsupervised monocular depth estimation with left-right consistency. In: CVPR (2017)Google Scholar
  10. 10.
    Godard, C., Mac Aodha, O., Firman, M., Brostow, G.J.: Digging into self-supervised monocular depth estimation. In: ICCV (2019)Google Scholar
  11. 11.
    Guo, X., Li, H., Yi, S., Ren, J., Wang, X.: Learning monocular depth by distilling cross-domain stereo networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 506–523. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01252-6_30CrossRefGoogle Scholar
  12. 12.
    Huguet, F., Devernay, F.: A variational method for scene flow estimation from stereo sequences. In: ICCV (2007)Google Scholar
  13. 13.
    Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: FlowNet 2.0: evolution of optical flow estimation with deep networks. In: CVPR (2017)Google Scholar
  14. 14.
    Ilg, E., Saikia, T., Keuper, M., Brox, T.: Occlusions, motion and depth boundaries with a generic network for disparity, optical flow or scene flow estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11216, pp. 626–643. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01258-8_38CrossRefGoogle Scholar
  15. 15.
    Jiang, H., Sun, D., Jampani, V., Lv, Z., Learned-Miller, E., Kautz, J.: SENSE: a shared encoder network for scene-flow estimation. In: ICCV (2019)Google Scholar
  16. 16.
    Kendall, A., et al.: End-to-end learning of geometry and context for deep stereo regression. In: ICCV (2017)Google Scholar
  17. 17.
    Kuznietsov, Y., Stuckler, J., Leibe, B.: Semi-supervised deep learning for monocular depth map prediction. In: CVPR (2017)Google Scholar
  18. 18.
    Lai, H.Y., Tsai, Y.H., Chiu, W.C.: Bridging stereo matching and optical flow via spatiotemporal correspondence. In: CVPR (2019)Google Scholar
  19. 19.
    Li, K., Malik, J.: Learning to optimize. arXiv preprint arXiv:1606.01885 (2016)
  20. 20.
    Liu, P., Lyu, M., King, I., Xu, J.: SelFlow: self-supervised learning of optical flow. In: CVPR (2019)Google Scholar
  21. 21.
    Lv, Z., Dellaert, F., Rehg, J.M., Geiger, A.: Taking a deeper look at the inverse compositional algorithm. In: CVPR (2019)Google Scholar
  22. 22.
    Ma, W.C., Wang, S., Hu, R., Xiong, Y., Urtasun, R.: Deep rigid instance scene flow. In: CVPR (2019)Google Scholar
  23. 23.
    Mayer, N., et al.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: CVPR (2016)Google Scholar
  24. 24.
    Meister, S., Hur, J., Roth, S.: UnFlow: unsupervised learning of optical flow with a bidirectional census loss. In: AAAI (2018)Google Scholar
  25. 25.
    Menze, M., Heipke, C., Geiger, A.: Joint 3D estimation of vehicles and scene flow. In: ISPRS Workshop on Image Sequence Analysis (ISA) (2015)Google Scholar
  26. 26.
    Menze, M., Heipke, C., Geiger, A.: Object scene flow. ISPRS J. Photogramm. Remote Sens. (JPRS) 140, 60–76 (2018)CrossRefGoogle Scholar
  27. 27.
    Pang, J., et al.: Zoom and learn: generalizing deep stereo matching to novel domains. In: CVPR (2018)Google Scholar
  28. 28.
    Poggi, M., Pallotti, D., Tosi, F., Mattoccia, S.: Guided stereo matching. In: CVPR (2019)Google Scholar
  29. 29.
    Sun, D., Yang, X., Liu, M.Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: CVPR (2018)Google Scholar
  30. 30.
    Tang, C., Tan, P.: BA-Net: Dense bundle adjustment network. arXiv preprint arXiv:1806.04807 (2018)
  31. 31.
    Tonioni, A., Poggi, M., Mattoccia, S., Di Stefano, L.: Unsupervised adaptation for deep stereo. In: ICCV (2017)Google Scholar
  32. 32.
    Tonioni, A., Rahnama, O., Joy, T., Stefano, L.D., Ajanthan, T., Torr, P.H.: Learning to adapt for stereo. In: CVPR (2019)Google Scholar
  33. 33.
    Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: CVPR (2019)Google Scholar
  34. 34.
    Vedula, S., Baker, S., Rander, P., Collins, R., Kanade, T.: Three-dimensional scene flow. In: ICCV (1999)Google Scholar
  35. 35.
    Vogel, C., Schindler, K., Roth, S.: Piecewise rigid scene flow. In: ICCV (2013)Google Scholar
  36. 36.
    Wang, Y., Wang, P., Yang, Z., Luo, C., Yang, Y., Xu, W.: UnOS: unified unsupervised optical-flow and stereo-depth estimation by watching videos. In: CVPR (2019)Google Scholar
  37. 37.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  38. 38.
    Wedel, A., Rabe, C., Vaudrey, T., Brox, T., Franke, U., Cremers, D.: Efficient dense scene flow from sparse or dense stereo data. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 739–751. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-88682-2_56CrossRefGoogle Scholar
  39. 39.
    Yin, Z., Shi, J.: GeoNet: unsupervised learning of dense depth, optical flow and camera pose. In: CVPR (2018)Google Scholar
  40. 40.
    Zhang, F., Prisacariu, V., Yang, R., Torr, P.H.: GA-Net: guided aggregation net for end-to-end stereo matching. In: CVPR (2019)Google Scholar
  41. 41.
    Zhong, Y., Li, H., Dai, Y.: Open-world stereo video matching with deep RNN. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 104–119. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01216-8_7CrossRefGoogle Scholar
  42. 42.
    Zhou, T., Brown, M., Snavely, N., Lowe, D.G.: Unsupervised learning of depth and ego-motion from video. In: CVPR (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Google ResearchMountain ViewUSA
  2. 2.ETH ZurichZÜrichSwitzerland

Personalised recommendations