mlsanityData to 14 Oct 2026

High Resolution Multi-Scale RAFT (Robust Vision Challenge 2022)

arXiv 2210.16900 ↗First posted 30 Oct 2022cs.CVcited by 12

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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

  1. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,415
  2. 2015FlowNet: Learning Optical Flow with Convolutional Networkscited by 1,059
  3. 2017PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volumecited by 968
  4. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 881
  5. 2017Playing for Benchmarkscited by 169

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 5; 2025: 1; 2026: 1.

12 papers.