NVlabs/PWC-Net
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume, CVPR 2018 (Oral) observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3003
- days_rel: n/a
- days_push: 1472
- n_releases_24m: 0
Adoption not part of the score
1736 stars · 365 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Official NVIDIA implementation of PWC-Net, a CNN for optical flow estimation using pyramid, warping, and cost volume, released with CVPR 2018. Provides Caffe and PyTorch implementations with pretrained models.
Use cases
- estimate optical flow between video frames
- compute dense motion fields for video analysis
- run pretrained optical flow model in pytorch
- reproduce CVPR 2018 optical flow results
- motion estimation for video stabilization
When to choose
- you need a well-known baseline optical flow model
- you want Caffe or PyTorch pretrained optical flow networks
When to avoid
- you need a permissive commercial license (CC BY-NC-SA only)
- you need actively maintained code or modern framework support
Facets
library · maturity maintenance
computer-vision machine-learning deep-learning computer-vision deep-learning python optical-flow pytorch caffe research-code non-commercial-license linux
1 source
- readme: https://github.com/NVlabs/PWC-Net · fetched 2026-08-28 · 0f95ee4ec546
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| NVlabs/PWC-Net | main | 32 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem