Page 307 - 《软件学报》2025年第4期
P. 307
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.