Papers › Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation
Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation
Junhwa Hur, Stefan Roth
Deep learning approaches to optical flow estimation have seen rapid progress over the recent years. One common trait of many networks is that they refine an initial flow estimate either through multiple stages or across the levels of a coarse-to-fine representation. While leading to more accurate results, the downside of this is an increased number of parameters. Taking inspiration from both classical energy minimization approaches as well as residual networks, we propose an iterative residual refinement (IRR) scheme based on weight sharing that can be combined with several backbone networks. It reduces the number of parameters, improves the accuracy, or even achieves both. Moreover, we show that integrating occlusion prediction and bi-directional flow estimation into our IRR scheme can further boost the accuracy. Our full network achieves state-of-the-art results for both optical flow and occlusion estimation across several standard datasets.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Optical Flow Estimation | KITTI 2012 | IRR-PWC | Average End-Point Error | 1.6 | #10 of 12 | Archive leaderboard | report |
| Optical Flow Estimation | KITTI 2015 | IRR-PWC | Fl-all | 7.65 | #13 of 18 | Archive leaderboard | report |
| Optical Flow Estimation | Sintel-clean | IRR-PWC | Average End-Point Error | 3.84 | #24 of 29 | Archive leaderboard | report |
| Optical Flow Estimation | Sintel-final | IRR-PWC | Average End-Point Error | 4.579 | #21 of 28 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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