High Resolution Multi-Scale RAFT (Robust Vision Challenge 2022)
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Abstract (arXiv)
In this report, we present our optical flow approach, MS-RAFT+, that won the Robust Vision Challenge 2022. It is based on the MS-RAFT method, which successfully integrates several multi-scale concepts into single-scale RAFT. Our approach extends this method by exploiting an additional finer scale for estimating the flow, which is made feasible by on-demand cost computation. This way, it can not only operate at half the original resolution, but also use MS-RAFT's shared convex upsampler to obtain full resolution flow. Moreover, our approach relies on an adjusted fine-tuning scheme during training. This in turn aims at improving the generalization across benchmarks. Among all participating methods in the Robust Vision Challenge, our approach ranks first on VIPER and second on KITTI, Sintel, and Middlebury, resulting in the first place of the overall ranking.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 5 of its 9 references that are in the map. First posted on arXiv 30 Oct 2022.
Builds on
5 of 9 references.
References · 5
- 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,415
- 2015FlowNet: Learning Optical Flow with Convolutional Networkscited by 1,059
- 2017PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volumecited by 968
- 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 881
- 2017Playing for Benchmarkscited by 169
Built on by
Citing papers per year: 2022: 1; 2023: 4; 2024: 5; 2025: 1; 2026: 1.
12 papers.
- 2023Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereocited by 172
- 2022CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flowcited by 166
- 2024MemFlow: Optical Flow Estimation and Prediction with Memorycited by 55
- 2023CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoningcited by 15
- 2023Win-Win: Training High-Resolution Vision Transformers from Two Windowscited by 12
- 2025RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereocited by 7
- 2024PLT-D3: A High-fidelity Dynamic Driving Simulation Dataset for Stereo Depth and Scene Flowcited by 3
- 2024ScaleFlow++: Robust and Accurate Estimation of 3D Motion from Videocited by 3
- 2023Multispectral Stereo-Image Fusion for 3D Hyperspectral Scene Reconstructioncited by 1
- 2024ScaleFlow++: Robust and Accurate Estimation of 3D Motion from Videocited by 0
- 2024Local Attention Transformers for High-Detail Optical Flow Upsamplingcited by 0
- 2026Motion-guided sparse correction enables expert-quality point tracking across diverse microscopy regimescited by 0
- 2026Motion-guided sparse correction enables expert-quality point tracking across diverse microscopy regimes28 May 2026
- 2025RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo14 May 2025
- 2024Local Attention Transformers for High-Detail Optical Flow Upsampling9 Dec 2024
- 2024ScaleFlow++: Robust and Accurate Estimation of 3D Motion from Video16 Sep 2024
- 2024ScaleFlow++: Robust and Accurate Estimation of 3D Motion from Video13 Jul 2024
- 2024PLT-D3: A High-fidelity Dynamic Driving Simulation Dataset for Stereo Depth and Scene Flow11 Jun 2024
- 2024MemFlow: Optical Flow Estimation and Prediction with Memory7 Apr 2024
- 2023Multispectral Stereo-Image Fusion for 3D Hyperspectral Scene Reconstruction15 Dec 2023
- 2023CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoning5 Nov 2023
- 2023Win-Win: Training High-Resolution Vision Transformers from Two Windows1 Oct 2023
- 2023Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo3 Mar 2023
- 2022CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow18 Nov 2022