# Multi-Scale RAFT: Combining Hierarchical Concepts for Learning-based Optical FLow Estimation

arXiv: https://arxiv.org/abs/2207.12163 · HTML: https://mlsanity.com/paper/2207.12163/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 25 Jul 2022.

## Builds on (most cited first, 17 of 17)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Optical Flow Estimation using a Spatial Pyramid Network](https://mlsanity.com/paper/1611.00850/) (2016)
- [Learning to Estimate Hidden Motions with Global Motion Aggregation](https://mlsanity.com/paper/2104.02409/) (2021)
- [Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation](https://mlsanity.com/paper/1904.05290/) (2019)
- [High-Resolution Optical Flow from 1D Attention and Correlation](https://mlsanity.com/paper/2104.13918/) (2021)
- [Learning Optical Flow from a Few Matches](https://mlsanity.com/paper/2104.02166/) (2021)
- [Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation](https://mlsanity.com/paper/2010.14851/) (2020)
- [PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation](https://mlsanity.com/paper/1904.06116/) (2019)
- [Detail Preserving Residual Feature Pyramid Modules for Optical Flow](https://mlsanity.com/paper/2107.10990/) (2021)
- [ResFPN: Residual Skip Connections in Multi-Resolution Feature Pyramid Networks for Accurate Dense Pixel Matching](https://mlsanity.com/paper/2006.12235/) (2020)
- [PRAFlow_RVC: Pyramid Recurrent All-Pairs Field Transforms for Optical Flow Estimation in Robust Vision Challenge 2020](https://mlsanity.com/paper/2009.06360/) (2020)

## Built on by (most cited first, 9 of 9)

- [Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo](https://mlsanity.com/paper/2303.01943/) (2023)
- [AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation](https://mlsanity.com/paper/2303.16493/) (2023)
- [CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoning](https://mlsanity.com/paper/2311.02661/) (2023)
- [Self-supervised AutoFlow](https://mlsanity.com/paper/2212.01762/) (2022)
- [RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo](https://mlsanity.com/paper/2505.09368/) (2025)
- [RGM: A Robust Generalizable Matching Model](https://mlsanity.com/paper/2310.11755/) (2023)
- [Removing Cost Volumes from Optical Flow Estimators](https://mlsanity.com/paper/2510.13317/) (2025)
- [MambaFlow: A Mamba-Centric Architecture for End-to-End Optical Flow Estimation](https://mlsanity.com/paper/2503.07046/) (2025)
- [Perturbed State Space Feature Encoders for Optical Flow with Event Cameras](https://mlsanity.com/paper/2504.10669/) (2025)

## Abstract (arXiv)

Many classical and learning-based optical flow methods rely on hierarchical concepts to improve both accuracy and robustness. However, one of the currently most successful approaches -- RAFT -- hardly exploits such concepts. In this work, we show that multi-scale ideas are still valuable. More precisely, using RAFT as a baseline, we propose a novel multi-scale neural network that combines several hierarchical concepts within a single estimation framework. These concepts include (i) a partially shared coarse-to-fine architecture, (ii) multi-scale features, (iii) a hierarchical cost volume and (iv) a multi-scale multi-iteration loss. Experiments on MPI Sintel and KITTI clearly demonstrate the benefits of our approach. They show not only substantial improvements compared to RAFT, but also state-of-the-art results -- in particular in non-occluded regions. Code will be available at https://github.com/cv-stuttgart/MS_RAFT.