Research map / Architectures and efficiency / RNNs and normalization
DIP: Deep Inverse Patchmatch for High-Resolution Optical Flow
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Combine propagation and local search instead of using either alone (ablation) | DIP optical flow · Sintel (train) Clean | EPE: 1.62 -> 1.26 | Helped |
| Combine propagation and local search instead of using either alone (ablation) | DIP optical flow · KITTI-15 (train) | EPE: 7.63 -> 4.89 | Helped |
| Increase local search radius from 1 to 2 (ablation) | DIP Patchmatch · Sintel (train) Final | EPE: 2.93 -> 2.83 | Helped |
| Increase local search radius from 2 to 3 (ablation) | DIP Patchmatch · Sintel (train) Final | EPE: 2.83 -> 2.85 | No clear effect |
| Increase propagation seeds from 4 to 8 (ablation) | DIP Patchmatch · KITTI-15 (train) | EPE: 4.41 -> 4.45 | Hurt |
| Replace standard propagation with inverse propagation (ablation) | DIP Patchmatch · KITTI-15 (train) | EPE: 4.41 -> 4.29 | Helped |
| Replace standard propagation with inverse propagation (ablation) | DIP Patchmatch · Sintel (train) Clean | EPE: 1.27 -> 1.30 | Hurt |
| Replace standard propagation with inverse propagation (ablation) | DIP Patchmatch · 1088×1920 | inference time: 432 -> 327 | Helped |
| Use DIP instead of RAFT in warm-start inference | optical flow estimation · Sintel (test) Clean, warm-start | EPE: 1.61 to 1.44 | Helped |
| Use DIP instead of RAFT | optical flow estimation · KITTI-15 (test) all pixels | F1-all: 5.10% to 4.21% | Helped |
Closest papers by meaning
- PatchBatch: A Batch Augmented Loss for Optical Flow (CVPR 2016 · similarity 0.88)
- CNN-Based Patch Matching for Optical Flow With Thresholded Hinge Embedding Loss (CVPR 2017 · similarity 0.87)
- Latent Refinement via Flow Matching for Training-free Linear Inverse Problem Solving (NeurIPS 2025 · similarity 0.85)
- Injecting Frame-Event Complementary Fusion into Diffusion for Optical Flow in Challenging Scenes (NeurIPS 2025 · similarity 0.85)
- DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework (CVPR 2025 · similarity 0.85)
- Neuroexplicit Diffusion Models for Inpainting of Optical Flow Fields (ICML 2024 · similarity 0.84)
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