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M-FUSE: Multi-frame Fusion for Scene Flow Estimation

, , , and . Proc. Winter Conference on Applications of Computer Vision (WACV), (January 2023)
DOI: 10.48550/arXiv.2207.05704

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Efficient Algorithms for Global Optimisation Methods in Computer Vision (Dagstuhl Seminar 11471)., , and . Dagstuhl Reports, 1 (11): 66-90 (2011)Adaptive Integration of Feature Matches into Variational Optical Flow Methods., , and . ACCV (3), volume 7726 of Lecture Notes in Computer Science, page 1-14. Springer, (2012)Variationsansätze zur Bewegungsschätzung: Präzise Modellierung und Effiziente Numerik.. Ausgezeichnete Informatikdissertationen, volume D-7 of LNI, GI, (2006)MS-RAFT+: High Resolution Multi-Scale RAFT, , , , and . International Journal of Computer Vision, (2023)Vision for autonomous vehicles and probes, , , and . 5, 11, page 36-61. Wadern, Schloss Dagstuhl, (2015)Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo, , , , and . Dataset, (2023)Related to: Lukas Mehl, Jenny Schmalfuss, Azin Jahedi, Yaroslava Nalivayko, Andrés Bruhn: Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo. Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. arXiv: 2303.01943.Multi-Scale Raft : Combining Hierarchical Concepts for Learning-Based Optical Flow Estimation, , , and . 2022 IEEE International Conference on Image Processing (ICIP), page 1236-1240. IEEE, (2023)M-FUSE : Multi-frame Fusion for Scene Flow Estimation, , , and . 2023 IEEE Winter Conference on Applications of Computer Vision, page 2019-2028. Piscataway, NJ, IEEE, (2023)Subjective annotation for a frame interpolation benchmark using artefact amplification, , , , and . Quality and User Experience, (2020)Article Number: 8.Visual Quality Assessment for Interpolated Slow-Motion Videos Based on a Novel Database, , , , and . 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), page 1-6. IEEE, (2020)