Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
[CVPR'22 Oral] GMFlow: Learning Optical Flow via Global Matching
| Date | Stars |
|---|---|
| 2026-07-24 | 795 |
| 2026-07-25 | 795 |
| 2026-07-28 | 795 |
| 2026-07-30 | 795 |
| 2026-08-06 | 795 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# GMFlow Official PyTorch implementation of paper: [**GMFlow: Learning Optical Flow via Global Matching**](https://arxiv.org/abs/2111.13680), **CVPR 2022, Oral** Authors: [Haofei Xu](https://haofeixu.github.io/), [Jing Zhang](https://scholar.google.com.hk/citations?user=9jH5v74AAAAJ), [Jianfei Cai](https://jianfei-cai.github.io/), [Hamid Rezatofighi](https://scholar.google.com/citations?user=VxAuxMwAAAAJ), [Dacheng Tao](https://scholar.google.com/citations?user=RwlJNLcAAAAJ) **11/15/2022 Update: Check out our new work: [Unifying Flow, Stereo and Depth Estimation](https://haofeixu.github.io/unimatch/) and code: [unimatch](https://github.com/autonomousvision/unimatch) for extending GMFlow to stereo and depth tasks. [More pretrained GMFlow models](https://github.com/autonomousvision/unimatch/blob/master/MODEL_ZOO.md) with different speed-accuracy trade-offs are also released. Check out our [Colab](https://colab.research.google.com/drive/1r5m-xVy3Kw60U-m5VB-aQ98oqqg_6cab?usp=sharing) and [HuggingFace](https://huggingface.co/spaces/haofeixu/unimatch) demo to play with GMFlow in your browser!** **A [video introduction](https://www.bilibili.com/video/BV18A4y1R7PL) (in Chinese) of GMFlow is available at bilibili!** https://user-images.githubusercontent.com/19343475/174446408-520b8a6c-9714-4ff3-978c-98e23ab29c1f.mp4 We streamline the optical flow estimation pipeline by reformulating optical flow as a **global matching** problem. <p align="center"><img width=90% src="assets/gmflow.png"></p> ## Highlights - **Flexible & Modular design** We decompose the end-to-end optical flow framework into five components: feature extraction, feature enhancement, feature matching, flow propagation and flow refinement. One can easily construct a customized optical flow model by combining different components. - **High accuracy** With only one refinement, GMFlow outperforms 31-refinements RAFT on the challenging Sintel benchmark. - **High efficiency** A basic GMFlow model (without refinement) runs at 57ms (V100) or 26ms (A100) for Sintel data (436x1024). GMFlow gains more speedup than RAFT on high-end GPUs (e.g., A100) since GMFlow doesn't require a large number of sequential computation. GMFlow also simplifies backward flow computation without requiring to forward the network twice. The bidirectional flow can be used for occlusion detection with forward-backward consistency check. <p align="center"><img width=90% src="assets/bidir_flow_occ.png"></p> ## Installation Our code is based on pytorch 1.9.0, CUDA 10.2 and python 3.8. Higher version pytorch should also work well. We recommend using [conda](https://www.anaconda.com/distribution/) for installation: ``` conda env create -f environment.yml conda activate gmflow ``` ## Demos All pretrained models can be downloaded from [google drive](https://drive.google.com/file/d/1d5C5cgHIxWGsFR1vYs5XrQbbUiZl9TX2/view?usp=sharing). You can run a trained model on a sequence of images and visualize the results: ``` CUDA_VISIBLE_DEVICES=0 python main.py \ --inference_dir demo/sintel_market_1 \ --output_path output/gmflow-norefine-sintel_market_1 \ --resume pretrained/gmflow_sintel-0c07dcb3.pth ``` You can also predict bidirectional flow with `--pred_bidir_flow` enabled and use `--fwd_bwd_consistency_check` for forward-backward consistency check. More examples can be found in [scripts/demo.sh](scripts/demo.sh). ## Datasets The datasets used to train and evaluate GMFlow are as follows: * [FlyingChairs](https://lmb.informatik.uni-freiburg.de/resources/datasets/FlyingChairs.en.html#flyingchairs) * [FlyingThings3D](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html) * [Sintel](http://sintel.is.tue.mpg.de/) * [KITTI](http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=flow) * [HD1K](http://hci-benchmark.iwr.uni-heidelberg.de/) By default the dataloader [datasets.py](data/datasets.py) assumes the datas
Excerpt of 6,807 characters
Read on GitHub
23
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a3bf4df8e58f9f82, topic:transformer