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Multi-View Stereo (MVS) is a core task in 3D computer vision.
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Y. Yao, Z. Luo, S. Li, T. Fang, and L. Quan, “MVSNet: Depth inference for unstructured multi-view stereo,” in European Conference on Computer Vision (ECCV) , 2018, pp. 767–783
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V. Sitzmann, M. Zollhöfer, and G. Wetzstein, “Scene representation networks: Continuous 3D-structure-aware neural scene representations,” in Advances in Neural Information Processing Systems (NeurIPS) , 2019
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B. Mildenhall, P. P. Srinivasan, R. Ortiz-Cayon, N. K. Kalantari, R. Ramamoorthi, R. Ng, and A. Kar, “Local light field fusion: Practical view synthesis with prescriptive sampling guidelines,” ACM Transactions on Graphics (TOG) , vol. 38, no. 4, pp. 1–14, 2019
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J. Wagner, J. M. Köhler, T. Gindele, L. Hetzel, J. T. Wiedemer, and S. Behnke, “Interpretable and fine-grained visual explanations for convolutional neural networks,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 9097–9107
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A. Yu, V. Ye, M. Tancik, and A. Kanazawa, “pixelNeRF: Neural radiance fields from one or few images,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021
2021
Closest in time.
Q. Wang, Z. Wang, K. Genova, P. Srinivasan, H. Zhou, J. T. Barron, R. Martin-Brualla, N. Snavely, and T. Funkhouser, “IBRNet: Learning multi-view image-based rendering,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021
2021
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F. Darmon, B. Bascle, J. Devaux, P. Monasse, and M. Aubry, “Deep multi-view stereo gone wild,” International Conference on 3D Vision , 2021
2021
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2021
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Cited alongside, same era.
B. Mildenhall, P. P. Srinivasan, R. Ortiz-Cayon, N. K. Kalantari, R. Ramamoorthi, R. Ng, and A. Kar, “Local light field fusion: Practical view synthesis with prescriptive sampling guidelines,” ACM Transactions on Graphics (TOG) , vol. 38, no. 4, pp. 29:1–29:14, 2019
2019
Cited alongside, same era.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “NeRF: Representing scenes as neural radiance fields for view synthesis,” in European Conference on Computer Vision (ECCV) , 2020
2020
Cited alongside, same era.
A. Rochow, M. Schwarz, M. Weinmann, and S. Behnke, “FaDIV-Syn: Fast depth-independent view synthesis,” in Proceedings of Robotics: Science and Systems (RSS) , 2022
2022
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