Research map / Probabilistic and scientific modeling / Molecules and proteins
Optical Flow Estimation Using a Spatial Pyramid Network
Check your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a coarse-to-fine spatial pyramid with image warping and learned residual-flow updates instead of a single end-to-end flow network | SPyNet · Sintel Clean test | EPE: 7.42 -> 6.69 | Helped |
| Use a coarse-to-fine spatial pyramid with image warping and learned residual-flow updates instead of a single end-to-end flow network | SPyNet · Sintel Final test | EPE: 8.43 -> 8.43 | No clear effect |
| Use a coarse-to-fine spatial pyramid with image warping and learned residual-flow updates instead of a single end-to-end flow network | SPyNet · Middlebury test | EPE: 0.32 -> 0.58 | Hurt |
| Use a coarse-to-fine spatial pyramid with image warping and learned residual-flow updates instead of a single end-to-end flow network | SPyNet · Flying Chairs test | EPE: 2.19 -> 2.63 | Hurt |
| Fine-tune on Sintel Clean | SPyNet · Sintel Clean train | EPE: 4.12 -> 3.17 | Helped |
| Fine-tune on Sintel Clean | SPyNet · Sintel Clean test | EPE: 6.69 -> 6.64 | Helped |
| Fine-tune on Sintel Final | SPyNet · Sintel Final test | EPE: 8.43 -> 8.36 | Helped |
| Fine-tune on Driving and Monkaa scenes | SPyNet · KITTI train | EPE: 9.12 -> 4.13 | Helped |
| Fine-tune on Driving and Monkaa scenes | SPyNet · KITTI test | EPE | Helped |
| Repeat the finest-level network G4 at a sixth pyramid level | SPyNet on Sintel · Sintel | EPE | Helped |
| Use six pyramid levels rather than five for Sintel | SPyNet on Sintel · Sintel | EPE | Helped |
Closest papers by meaning
- LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation (CVPR 2018 · similarity 0.92)
- FlowNet 2.0: Evolution of Optical Flow Estimation With Deep Networks (CVPR 2017 · similarity 0.92)
- SKFlow: Learning Optical Flow with Super Kernels (NeurIPS 2022 · similarity 0.90)
- Recurrent Partial Kernel Network for Efficient Optical Flow Estimation (AAAI 2024 · similarity 0.90)
- Unsupervised Deep Learning for Optical Flow Estimation (AAAI 2017 · similarity 0.89)
- DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework (CVPR 2025 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.