Page 306 - 《软件学报》2025年第4期

1712 软件学报 2025 年第 36 卷第 4 期

[41] Li Y, Zhao ZJ, Fan JH, Li WY. ADR-MVSNet: A cascade network for 3D point cloud reconstruction with pixel occlusion. Pattern
Recognition, 2022, 125: 108516. [doi: 10.1016/j.patcog.2021.108516]
[42] Yi HW, Wei ZZ, Ding MY, Zhang RZ, Chen YS, Wang GP, Tai YW. Pyramid multi-view stereo net with self-adaptive view aggregation.
In: Proc. of the 2020 European Conf. on Computer Vision. Glasgow: Springer, 2020. 766–782. [doi: 10.1007/978-3-030-58545-7_44]
[43] Peng R, Wang RJ, Wang ZY, Lai YW, Wang RG. Rethinking depth estimation for multi-view stereo: A unified representation. In: Proc.
of the 2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 8635–8644. [doi: 10.1109/
CVPR52688.2022.00845]
[44] Wang XF, Zhu Z, Huang G, Qin FB, Ye Y, He YJ, Chi X, Wang XG. MVSTER: Epipolar transformer for efficient multi-view stereo. In:
Proc. of the 17th European Conf. on Computer Vision. Tel Aviv: Springer, 2022. 573–591. [doi: 10.1007/978-3-031-19821-2_33]
[45] Liao JL, Ding YK, Shavit Y, Huang DH, Ren SH, Guo J, Feng WS, Zhang K. WT-MVSNet: Window-based Transformers for multi-view
stereo. Advances in Neural Information Processing Systems, 2022, 35: 8564–8576.
[46] Liu YM, Rao Y, Rigall E, Fan H, Dong JY. Incorporating co-visibility reasoning into surface depth measurement. IEEE Trans. on
Instrumentation and Measurement, 2023, 72: 5009912. [doi: 10.1109/TIM.2023.3250231]
[47] Xu QS, Tao WB. Learning inverse depth regression for multi-view stereo with correlation cost volume. In: Proc. of the 34th AAAI Conf.
on Artificial Intelligence. New York: AAAI, 2020. 12508–12515. [doi: 10.1609/aaai.v34i07.6939]

[48] Gu XD, Fan ZW, Zhu SY, Dai ZZ, Tan FT, Tan P. Cascade cost volume for high-resolution multi-view stereo and stereo matching. In:
Proc. of the 2020 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020. 2492–2501. [doi: 10.1109/
CVPR42600.2020.00257]
[49] Cheng S, Xu ZX, Zhu SL, Li ZW, Li LE, Ramamoorthi R, Su H. Deep stereo using adaptive thin volume representation with uncertainty
awareness. In: Proc. of the 2020 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020. 2521–2531. [doi: 10.
1109/CVPR42600.2020.00260]
[50] Ma XJ, Gong Y, Wang QR, Huang JW, Chen L, Yu F. EPP-MVSNet: Epipolar-assembling based depth prediction for multi-view stereo.
In: Proc. of the 2021 IEEE/CVF Int’l Conf. on Computer Vision. Montreal: IEEE, 2021. 5712–5720. [doi: 10.1109/ICCV48922.2021.
00568]
[51] Yang JY, Alvarez JM, Liu MM. Non-parametric depth distribution modelling based depth inference for multi-view stereo. In: Proc. of the
2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 8616–8624. [doi: 10.1109/CVPR52688.
2022.00843]
[52] Yao Y, Luo ZX, Li SW, Shen TW, Fang T, Quan L. Recurrent MVSNet for high-resolution multi-view stereo depth inference. In: Proc.
of the 2019 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019. 5520–5529. [doi: 10.1109/CVPR.
2019.00567]
[53] Chen R, Han SF, Xu J, Su H. Visibility-aware point-based multi-view stereo network. IEEE Trans. on Pattern Analysis and Machine
Intelligence, 2021, 43(10): 3695–3708. [doi: 10.1109/TPAMI.2020.2988729]
[54] Weilharter R, Fraundorfer F. HighRes-MVSNet: A fast multi-view stereo network for dense 3D reconstruction from high-resolution
images. IEEE Access, 2021, 9: 11306–11315. [doi: 10.1109/ACCESS.2021.3050556]
[55] Luo KY, Guan T, Ju LL, Huang HP, Luo YW. P-MVSNet: Learning patch-wise matching confidence aggregation for multi-view stereo.
In: Proc. of the 2019 IEEE/CVF Int’l Conf. on Computer Vision. Seoul: IEEE, 2019. 10451–10460. [doi: 10.1109/ICCV.2019.01055]
[56] Yu ZH, Gao SH. Fast-MVSNet: Sparse-to-dense multi-view stereo with learned propagation and gauss-newton refinement. In: Proc. of
the 2020 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020. 1946–1955. [doi: 10.1109/CVPR42600.
2020.00202]
[57] Wang FJH, Galliani S, Vogel C, Pollefeys M. IterMVS: Iterative probability estimation for efficient multi-view stereo. In: Proc. of the
2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 8596–8605. [doi: 10.1109/CVPR52688.
2022.00841]
[58] Rich A, Stier N, Sen P, Höllerer T. 3DVNet: Multi-view depth prediction and volumetric refinement. In: Proc. of the 2021 Int’l Conf. on
3D Vision. London: IEEE, 2021. 700–709. [doi: 10.1109/3DV53792.2021.00079]
2
[59] Dai YC, Zhu ZD, Rao ZB, Li B. MVS : Deep unsupervised multi-view stereo with multi-view symmetry. In: Proc. of the 2019 Int’l Conf.
on 3D Vision. Quebec City: IEEE, 2019. 1–8. [doi: 10.1109/3DV.2019.00010]
[60] Mallick A, Stückler J, Lensch HPA. Learning to adapt multi-view stereo by self-supervision. In: Proc. of the 31st British Machine Vision
Conf. BMVA Press, 2020. [doi: 10.5555/3600270.3600893]
3
[61] Huang BC, Yi HW, Huang C, He YJ, Liu JB, Liu X. M VSNET: Unsupervised multi-metric multi-view stereo network. In: Proc. of the
2021 IEEE Int’l Conf. on Image Processing. Anchorage: IEEE, 2021. 3163–3167. [doi: 10.1109/ICIP42928.2021.9506469]