PAR-mono: monocular video depth estimation network based on channel separation and dynamic attention (original) (raw)

References

  1. Zhang, Z., Xu, C., Yang, J., Tai, Y., Chen, L.: Deep hierarchical guidance and regularization learning for end-to-end depth estimation. Pattern Recogn. 83, 430–442 (2018). https://doi.org/10.1016/j.patcog.2018.05.016
    Article MATH Google Scholar
  2. Yu, F., Zhang, G., Zhao, F., Wang, X., Liu, H., Lin, P., Chen, Y.: Improved yolo-v5 model for boosting face mask recognition accuracy on heterogeneous iot computing platforms. Internet Things. 23, 100881 (2023). https://doi.org/10.1016/j.iot.2023.100881
    Article Google Scholar
  3. Li, S., Huang, H., Meng, X., Wang, M., Li, Y., Xie, L.: A glove-wearing detection algorithm based on improved yolov8. Sensors (2023). https://doi.org/10.3390/s23249906
    Article MATH Google Scholar
  4. Zhang, J., Ma, P., Jiang, T., Zhao, X., Tan, W., Zhang, J., Zou, S., Huang, X., Grzegorzek, M., Li, C.: Sem-rcnn: a squeeze-and-excitation-based mask region convolutional neural network for multi-class environmental microorganism detection. Appl. Sci. (2022). https://doi.org/10.3390/app12199902
    Article Google Scholar
  5. Wu, W., Chu, T., Liu, Q.: Complementarity-aware cross-modal feature fusion network for rgb-t semantic segmentation. Pattern Recogn. 131, 108881 (2022). https://doi.org/10.1016/j.patcog.2022.108881
    Article Google Scholar
  6. Zhang, X., Quan, Z., Li, Q., Zhu, D., Yang, W.: Sed: searching enhanced decoder with switchable skip connection for semantic segmentation. Pattern Recogn. 149, 110196 (2024). https://doi.org/10.1016/j.patcog.2023.110196
    Article Google Scholar
  7. Fu, H., Meng, D., Li, W., Wang, Y.: Bridge crack semantic segmentation based on improved deeplabv3+. J. Mar. Sci. Eng. (2021). https://doi.org/10.3390/jmse9060671
    Article MATH Google Scholar
  8. Zou, L., Huang, Z., Gu, N., Wang, G.: Learning geometric consistency and discrepancy for category-level 6d object pose estimation from point clouds. Pattern Recogn. 145, 109896 (2024). https://doi.org/10.1016/j.patcog.2023.109896
    Article Google Scholar
  9. Casser, V., Pirk, S., Mahjourian, R., Angelova, A.: Unsupervised monocular depth and ego-motion learning with structure and semantics. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 381–388 (2019). https://doi.org/10.1109/CVPRW.2019.00051
  10. Zhou, Z., Fan, X., Shi, P., Xin, Y.: R-msfm: recurrent multi-scale feature modulation for monocular depth estimating. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 12757–12766 (2021). https://doi.org/10.1109/ICCV48922.2021.01254
  11. Klingner, M., Termóhlen, J.-A., Mikolajczyk, J., Fingscheidt, T.: Self-supervised monocular depth estimation: solving the dynamic object problem by semantic guidance. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) Computer Vision–ECCV 2020, pp. 582–600. Springer, Cham (2020)
    Chapter Google Scholar
  12. Lyu, X., Liu, L., Wang, M., Kong, X., Liu, L., Liu, Y., Chen, X., Yuan, Y.: Hr-depth: high resolution self-supervised monocular depth estimation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2294–2301 (2021). https://doi.org/10.1609/aaai.v35i3.16329
  13. Zhou, T., Brown, M., Snavely, N., Lowe, D.G.: Unsupervised learning of depth and ego-motion from video. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6612–6619 (2017). https://doi.org/10.1109/CVPR.2017.700
  14. Guizilini, V.C., Hou, R., Li, J., Ambrus, R., Gaidon., A.: Semantically-guided representation learning for self-supervised monocular depth. arXiv abs/2002.12319 (2020)
  15. Klingner, M., Termóhlen, J.-A., Mikolajczyk, J., Fingscheidt, T.: Self-supervised monocular depth estimation: solving the dynamic object problem by semantic guidance. In: Computer Vision—ECCV 2020, pp. 582–600. Springer, Berlin, Heidelberg (2020). https://doi.org/10.1007/978-3-030-58565-5_35
  16. Ranjan, A., Jampani, V., Balles, L., Kim, K., Sun, D., Wulff, J., Black, M.J.: Competitive collaboration: joint unsupervised learning of depth, camera motion, optical flow and motion segmentation. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12232–12241 (2019). https://doi.org/10.1109/CVPR.2019.01252
  17. Bian, J., Zhan, H., Wang, N., Li, Z., Zhang, L., Shen, C., Cheng, M.-M.: Unsupervised scale-consistent depth learning from video. Int. J. Comput. Vis. 129, 2548–2564 (2021)
    Article MATH Google Scholar
  18. Sun, L., Bian, J., Zhan, H., Yin, W., Reid, I.D., Shen, C.: Sc-depthv3: Robust self-supervised monocular depth estimation for dynamic scenes. IEEE Trans. Pattern Anal. Mach. Intell. 46, 497–508 (2022)
    Article Google Scholar
  19. Zhang, N., Nex, F., Vosselman, G., Kerle, N.: Lite-mono: a lightweight cnn and transformer architecture for self-supervised monocular depth estimation. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18537–18546 (2022)
  20. Krizhevsky, A., Sutskever, I.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2012)
    Article MATH Google Scholar
  21. Liu, F., Shen, C., Lin, G., Reid, I.: Learning depth from single monocular images using deep convolutional neural fields. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2024–2039 (2016). https://doi.org/10.1109/TPAMI.2015.2505283
    Article MATH Google Scholar
  22. Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27. Curran Associates Inc., USA (2014)
    MATH Google Scholar
  23. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90
  24. Laina, I., Rupprecht, C., Belagiannis, V., Tombari, F., Navab, N.: Deeper depth prediction with fully convolutional residual networks. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 239–248 (2016). https://doi.org/10.1109/3DV.2016.32
  25. Song, M., Lim, S., Kim, W.: Monocular depth estimation using Laplacian pyramid-based depth residuals. IEEE Trans. Circuits Syst. Video Technol. 31(11), 4381–4393 (2021). https://doi.org/10.1109/TCSVT.2021.3049869
    Article MATH Google Scholar
  26. Xia, Z., Sullivan, P., Chakrabarti, A.: Generating and exploiting probabilistic monocular depth estimates. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 62–71 (2019)
  27. Garg, R., Vijaya Kumar, B.G., Carneiro, G., Reid, I.: Unsupervised cnn for single view depth estimation: geometry to the rescue. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision–ECCV 2016, pp. 740–756. Springer, Cham (2016)
    Chapter Google Scholar
  28. Godard, C., Aodha, O.M., Brostow., G.J.: Unsupervised monocular depth estimation with left-right consistency. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6602–6611 (2016)
  29. Love, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)
    Article Google Scholar
  30. Mur-Artal, R., Tardós, J.D.: Orb-slam2: an open-source slam system for monocular, stereo, and rgb-d cameras. IEEE Trans. Rob. 33(5), 1255–1262 (2017). https://doi.org/10.1109/TRO.2017.2705103
    Article Google Scholar
  31. Kim, D., Park, J., Ko, K.H.: Development of an ar based method for augmentation of 3d cad data onto a real ship block image. Computer-Aided Des. 98, 1–11 (2018). https://doi.org/10.1016/j.cad.2017.12.003
    Article MATH Google Scholar
  32. Kendall, A., Grimes, M.K., Cipolla, R.: Posenet: a convolutional network for real-time 6-dof camera relocalization. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 2938–2946 (2015)
  33. Zhang, R., Luo, Z., Dhanjal, S.S., Schmotzer, C., Hasija, S.: Posenet + + : A CNN Framework for Online Pose Regression and Robot Re-localization (2018)
  34. Huang, Z., Xu, Y., Shi, J., Zhou, X., Bao, H., Zhang, G.: Prior guided dropout for robust visual localization in dynamic environments. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2791–2800 (2019). https://doi.org/10.1109/ICCV.2019.00288
  35. Tian, M., Nie, Q., Shen, H.: 3d scene geometry-aware constraint for camera localization with deep learning. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 4211–4217 (2020)
  36. Xian, K., Zhang, J., Wang, O., Mai, L., Lin, Z., Cao, Z.: Structure-guided ranking loss for single image depth prediction. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 608–617 (2020). https://doi.org/10.1109/CVPR42600.2020.00069
  37. Godard, C., Aodha, O.M., Firman, M., Brostow, G.: Digging into self-supervised monocular depth estimation. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3827–3837 (2019). https://doi.org/10.1109/ICCV.2019.00393
  38. Bian, J.-W., Zhan, H., Wang, N., Chin, T.-J., Shen, C., Reid, I.: Auto-rectify network for unsupervised indoor depth estimation. IEEE Trans. Pattern Anal. Mach. Intell. 44(12), 9802–9813 (2022). https://doi.org/10.1109/TPAMI.2021.3136220
    Article MATH Google Scholar

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