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樊铭瑞 等: 基于深度学习的多视图立体视觉综述                                                         1713


                 [62]  Yang JY, Alvarez JM, Liu MM. Self-supervised learning of depth inference for multi-view stereo. In: Proc. of the 2021 IEEE/CVF Conf.
                     on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021. 7522–7530. [doi: 10.1109/CVPR46437.2021.00744]
                 [63]  Xu HB, Zhou ZP, Wang YL, Kang WX, Sun BG, Li H, Qiao Y. Digging into uncertainty in self-supervised multi-view stereo. In: Proc. of
                     the 2021 IEEE/CVF Int’l Conf. on Computer Vision. Montreal: IEEE, 2021. 6058–6067. [doi: 10.1109/ICCV48922.2021.00602]
                 [64]  Dong  HN,  Yao  J.  PatchMVSNet:  Patch-wise  unsupervised  multi-view  stereo  for  weakly-textured  surface  reconstruction.  arXiv:2203.
                     02156, 2022.
                 [65]  Xu HB, Zhou ZP, Qiao Y, Kang WX, Wu QX. Self-supervised multi-view stereo via effective co-segmentation and data-augmentation.
                     In: Proc. of the 35th AAAI Conf. on Artificial Intelligence. AAAI, 2021. 3030–3038. [doi: 10.1609/aaai.v35i4.16411]
                 [66]  Ding YK, Zhu QT, Liu XY, Yuan WT, Zhang HT, Zhang C. KD-MVS: Knowledge distillation based self-supervised learning for multi-
                     view stereo. In: Proc. of the 17th European Conf. on Computer Vision. Tel Aviv: Springer, 2022. 630–646. [doi: 10.1007/978-3-031-
                     19821-2_36]
                 [67]  Mildenhall B, Srinivasan PP, Tancik M, Barron JT, Ramamoorthi R, Ng R. NeRF: Representing scenes as neural radiance fields for view
                     synthesis. In: Proc. of the 16th European Conf. on Computer Vision. Glasgow: Springer, 2020. 405–421. [doi: 10.1007/978-3-030-58452-
                     8_24]
                 [68]  Rosu RA, Behnke S. NeuralMVS: Bridging multi-view stereo and novel view synthesis. In: Proc. of the 2022 Int’l Joint Conf. on Neural
                     (TOG), 2017, 36(4): 78. [doi: 10.1145/3072959.3073599]
                     Networks. Padua: IEEE, 2022. 1–7. [doi: 10.1109/IJCNN55064.2022.9892024]
                 [69]  Xi JH, Shi YF, Wang YJ, Guo YL, Xu K. RayMVSNet: Learning ray-based 1D implicit fields for accurate multi-view stereo. In: Proc. of
                     the  2022  IEEE/CVF  Conf.  on  Computer  Vision  and  Pattern  Recognition.  New  Orleans:  IEEE,  2022.  8585–8595.  [doi:  10.1109/
                     CVPR52688.2022.00840]
                 [70]  Chang D, Božič A, Zhang T, Yan QS, Chen YC, Süsstrunk S, Nießner M. RC-MVSNet: Unsupervised multi-view stereo with neural
                     rendering. In: Proc. of the 17th European Conf. on Computer Vision. Tel Aviv: Springer, 2022. 665–680. [doi: 10.1007/978-3-031-19821-
                     2_38]
                 [71]  Wang P, Liu LJ, Liu Y, Theobalt C, Komura T, Wang WP. NeuS: Learning neural implicit surfaces by volume rendering for multi-view
                     reconstruction. In: Proc. of the 35th Int’l Conf. on Neural Information Processing Systems. Curran Associates Inc., 2021. 2081.
                 [72]  Yariv  L,  Gu  JT,  Kasten  Y,  Lipman  Y.  Volume  rendering  of  neural  implicit  surfaces.  In:  Proc.  of  the  35th  Int’l  Conf.  on  Neural
                     Information Processing Systems. Curran Associates Inc., 2021. 367.
                 [73]  Oechsle M, Peng SY, Geiger A. UNISURF: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction. In: Proc.
                     of the 2021 IEEE/CVF Int’l Conf. on Computer Vision. Montreal: IEEE, 2021. 5569–5579. [doi: 10.1109/ICCV48922.2021.00554]
                 [74]  Long XX, Lin C, Wang P, Komura T, Wang WP. SparseNeuS: Fast generalizable neural surface reconstruction from sparse views. In:
                     Proc. of the 17th European Conf. on Computer Vision. Tel Aviv: Springer, 2022. 210–227. [doi: 10.1007/978-3-031-19824-3_13]
                 [75]  Wu HY, Graikos A, Samaras D. S-VolSDF: Sparse multi-view stereo regularization of neural implicit surfaces. In: Proc. of the 2023
                     IEEE/CVF Int’l Conf. on Computer Vision. Paris: IEEE, 2023. 3533–3545. [doi: 10.1109/ICCV51070.2023.00329]
                 [76]  Ren YF, Wang FJH, Zhang T, Pollefeys M, Süsstrunk S. VolRecon: Volume rendering of signed ray distance functions for generalizable
                     multi-view reconstruction. In: Proc. of the 2023 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023.
                     16685–16695. [doi: 10.1109/CVPR52729.2023.01601]
                 [77]  Kerbl B, Kopanas G, Leimkuehler T, Drettakis G. 3D Gaussian splatting for real-time radiance field rendering. ACM Trans. on Graphics
                     (TOG), 2023, 42(4): 139. [doi: 10.1145/3592433]
                 [78]  Seitz SM, Curless B, Diebel J, Scharstein D, Szeliski R. A comparison and evaluation of multi-view stereo reconstruction algorithms. In:
                     Proc. of the 2006 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition. New York: IEEE, 2006. 519–528. [doi: 10.
                     1109/CVPR.2006.19]
                 [79]  Strecha C, von Hansen W, Van Gool L, Fua P, Thoennessen U. On benchmarking camera calibration and multi-view stereo for high
                     resolution imagery. In: Proc. of the 2008 IEEE Conf. on Computer Vision and Pattern Recognition. Anchorage: IEEE, 2008. 1–8. [doi: 10.
                     1109/CVPR.2008.4587706]
                 [80]  Knapitsch A, Park J, Zhou QY, Koltun V. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Trans. on Graphics

                 [81]  Schöps  T,  Schönberger  JL,  Galliani  S,  Sattler  T,  Schindler  K,  Pollefeys  M,  Geiger  A.  A  multi-view  stereo  benchmark  with  high-
                     resolution images and multi-camera videos. In: Proc. of the 2017 IEEE Conf. on Computer Vision and Pattern Recognition. Honolulu:
                     IEEE, 2017. 2538–2547. [doi: 10.1109/CVPR.2017.272]
                 [82]  Yao Y, Luo ZX, Li SW, Zhang JY, Ren YF, Zhou L, Fang T, Quan L. BlendedMVS: A large-scale dataset for generalized multi-view
                     stereo networks. In: Proc. of the 2020 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020. 1787–1796.
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