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Context-Guided Multi-view Stereo with Depth Back-Projection

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MultiMedia Modeling (MMM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13834))

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Abstract

Depth map based Multi-view stereo (MVS) is a task that focuses on taking images from multiple views of one same scene as input, estimating depth in each view, and generating 3D reconstructions of objects in the scene. Though most matching based MVS methods take features of the input images into account, few of them make the best of the underlying global information in images. They may suffer from difficult image regions, such as object boundaries, low-texture areas, and reflective surfaces. Human beings perceive these cases with the help of global awareness, that is to say, the context of the objects we observe. Similarly, we propose Context-guided Multi-view Stereo (ContextMVS), a coarse-to-fine pyramidal MVS network, which explicitly utilizes the context guidance in asymmetrical features to integrate global information into the 3D cost volume for feature matching. Also, with a low computational overhead, we adopt a depth back-projection refined up-sampling module to improve the non-parametric depth up-sampling between pyramid levels. Experimental results indicate that our method outperforms classical learning-based methods by a large margin on public benchmarks, DTU and Tanks and Temples, demonstrating the effectiveness of our method.

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References

  1. Aanæs, H., Jensen, R.R., Vogiatzis, G., Tola, E., Dahl, A.B.: Large-scale data for multiple-view stereopsis. Int. J. Comput. Vis. 120(2), 153–168 (2016). https://doi.org/10.1007/s11263-016-0902-9

    Article  MathSciNet  Google Scholar 

  2. Campbell, N.D.F., Vogiatzis, G., Hernández, C., Cipolla, R.: Using multiple hypotheses to improve depth-maps for multi-view stereo. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 766–779. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88682-2_58

    Chapter  Google Scholar 

  3. Cheng, S., et al.: Deep stereo using adaptive thin volume representation with uncertainty awareness. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2524–2534 (2020)

    Google Scholar 

  4. Ding, Y., et al.: TransMVSNet: global context-aware multi-view stereo network with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8585–8594 (2022)

    Google Scholar 

  5. Fua, P., Leclerc, Y.G.: Object-centered surface reconstruction: combining multi-image stereo and shading. Int. J. Comput. Vis. 16(1), 35–56 (1995)

    Article  Google Scholar 

  6. Furukawa, Y., Ponce, J.: Accurate, dense, and robust multiview stereopsis. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1362–1376 (2009)

    Article  Google Scholar 

  7. Galliani, S., Lasinger, K., Schindler, K.: Massively parallel multiview stereopsis by surface normal diffusion. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 873–881 (2015)

    Google Scholar 

  8. Gao, G., et al.: Neural image compression via attentional multi-scale back projection and frequency decomposition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14677–14686 (2021)

    Google Scholar 

  9. Gu, X., Fan, Z., Zhu, S., Dai, Z., Tan, F., Tan, P.: Cascade cost volume for high-resolution multi-view stereo and stereo matching. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2495–2504 (2020)

    Google Scholar 

  10. Haris, M., Shakhnarovich, G., Ukita, N.: Deep back-projection networks for super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1664–1673 (2018)

    Google Scholar 

  11. Huang, P.H., Matzen, K., Kopf, J., Ahuja, N., Huang, J.B.: DeepMVS: learning multi-view stereopsis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2821–2830 (2018)

    Google Scholar 

  12. Ji, M., Gall, J., Zheng, H., Liu, Y., Fang, L.: SurfaceNet: an end-to-end 3D neural network for multiview stereopsis. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2307–2315 (2017)

    Google Scholar 

  13. Jiang, S., Campbell, D., Lu, Y., Li, H., Hartley, R.: Learning to estimate hidden motions with global motion aggregation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9772–9781 (2021)

    Google Scholar 

  14. Kar, A., Häne, C., Malik, J.: Learning a multi-view stereo machine. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  15. Knapitsch, A., Park, J., Zhou, Q.Y., Koltun, V.: Tanks and temples: benchmarking large-scale scene reconstruction. ACM Trans. Graph. (ToG) 36(4), 1–13 (2017)

    Article  Google Scholar 

  16. Kutulakos, K.N., Seitz, S.M.: A theory of shape by space carving. Int. J. Comput. Vis. 38(3), 199–218 (2000). https://doi.org/10.1023/A:1008191222954

    Article  MATH  Google Scholar 

  17. Lhuillier, M., Quan, L.: A quasi-dense approach to surface reconstruction from uncalibrated images. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 418–433 (2005)

    Article  Google Scholar 

  18. Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022)

    Google Scholar 

  19. Long, X., Liu, L., Li, W., Theobalt, C., Wang, W.: Multi-view depth estimation using epipolar spatio-temporal networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8258–8267 (2021)

    Google Scholar 

  20. Luo, K., Guan, T., Ju, L., Huang, H., Luo, Y.: P-MVSNet: learning patch-wise matching confidence aggregation for multi-view stereo. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10452–10461 (2019)

    Google Scholar 

  21. Ma, X., Gong, Y., Wang, Q., Huang, J., Chen, L., Yu, F.: EPP-MVSNet: Epipolar-assembling based depth prediction for multi-view stereo. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5732–5740 (2021)

    Google Scholar 

  22. Mi, Z., Di, C., Xu, D.: Generalized binary search network for highly-efficient multi-view stereo. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12991–13000 (2022)

    Google Scholar 

  23. Peng, R., Wang, R., Wang, Z., Lai, Y., Wang, R.: Rethinking depth estimation for multi-view stereo: a unified representation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8645–8654 (2022)

    Google Scholar 

  24. Schonberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4104–4113 (2016)

    Google Scholar 

  25. Schönberger, J.L., Zheng, E., Frahm, J.-M., Pollefeys, M.: Pixelwise view selection for unstructured multi-view stereo. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 501–518. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_31

    Chapter  Google Scholar 

  26. Seitz, S.M., Dyer, C.R.: Photorealistic scene reconstruction by voxel coloring. Int. J. Comput. Vis. 35(2), 151–173 (1999)

    Article  Google Scholar 

  27. Teed, Z., Deng, J.: RAFT: recurrent all-pairs field transforms for optical flow. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 402–419. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58536-5_24

    Chapter  Google Scholar 

  28. Wang, F., Galliani, S., Vogel, C., Speciale, P., Pollefeys, M.: PatchmatchNet: learned multi-view patchmatch stereo. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14194–14203 (2021)

    Google Scholar 

  29. Wei, Z., Zhu, Q., Min, C., Chen, Y., Wang, G.: AA-RMVSNet: adaptive aggregation recurrent multi-view stereo network. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6187–6196 (2021)

    Google Scholar 

  30. Yan, J., et al.: Dense hybrid recurrent multi-view stereo net with dynamic consistency checking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 674–689. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58548-8_39

    Chapter  Google Scholar 

  31. Yang, J., Mao, W., Alvarez, J.M., Liu, M.: Cost volume pyramid based depth inference for multi-view stereo. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4877–4886 (2020)

    Google Scholar 

  32. Yao, Y., Luo, Z., Li, S., Fang, T., Quan, L.: MVSNet: depth inference for unstructured multi-view stereo. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 785–801. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01237-3_47

    Chapter  Google Scholar 

  33. Yao, Y., Luo, Z., Li, S., Shen, T., Fang, T., Quan, L.: Recurrent MVSNet for high-resolution multi-view stereo depth inference. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5525–5534 (2019)

    Google Scholar 

  34. Yao, Y., et al.: BlendedMVS: a large-scale dataset for generalized multi-view stereo networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1790–1799 (2020)

    Google Scholar 

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Acknowledgements

This work is supported by National Natural Science Foundation of China U21B2012 and 62072013, Shenzhen Cultivation of Excellent Scientific and Technological Innovation Talents RCJC20200714114435057, Shenzhen Research Projects of 201806080921419290.

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Authors and Affiliations

  1. Peking University Shenzhen Graduate School, Shenzhen, China

    Tianxing Feng, Zhe Zhang, Kaiqiang Xiong & Ronggang Wang

  2. Peng Cheng Laboratory, Shenzhen, China

    Ronggang Wang

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  1. Tianxing Feng
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  2. Zhe Zhang
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  3. Kaiqiang Xiong
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  4. Ronggang Wang
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Correspondence to Ronggang Wang .

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Editors and Affiliations

  1. University of Bergen, Bergen, Norway

    Duc-Tien Dang-Nguyen

  2. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  3. Radboud University Nijmegen, Nijmegen, The Netherlands

    Martha Larson

  4. Dublin City University, Dublin, Ireland

    Alan F. Smeaton

  5. University of Amsterdam, Amsterdam, The Netherlands

    Stevan Rudinac

  6. National Institute of Information and Communications Technology, Tokyo, Japan

    Minh-Son Dao

  7. Department of Information Science and Media Studies, University of Bergen, Bergen, Norway

    Christoph Trattner

  8. La Trobe University, Melbourne, VIC, Australia

    Phoebe Chen

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Feng, T., Zhang, Z., Xiong, K., Wang, R. (2023). Context-Guided Multi-view Stereo with Depth Back-Projection. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13834. Springer, Cham. https://doi.org/10.1007/978-3-031-27818-1_8

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  • DOI: https://doi.org/10.1007/978-3-031-27818-1_8

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