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Pointly-Supervised Panoptic Segmentation (ECCV 2022 Oral)

Panoptic segmentation with a single point label per target! [paper]

Here are all the codes to reproduce the results. We are still cleaning them and will update later.

The code is based on the implementation of Panoptic SegFormer.

The point labels used in the paper can be downloaded here.

Install

0. Install common prerequisties: Python, PyTorch, CUDA, etc.
1. Install mmcv. This work is tested with mmcv up to version 1.7.1.
2. Install other requirements:

   pip install -r requirements.txt
   

3. Install the custom pydijkstra package:

   cd PSPS/py-dijkstra/pydijkstra
   python setup.py install --user
   

Note that the panopticapi has risk of memory leakage, as been discussed in this issue. The solution is to add a workers.close() in panopticapi/evaluation.py/pq_compute_multi_core before the function return.

Prepare Dataset

• Pascal VOC

  1. Download the Pascal VOC dataset.
  2. Download the point labels here.
  3. Organize the dataset by the following structure:

     PSPS/data/voc
     ├── JPEGImages
     │   ├── 20xx_xxxxxx.jpg
     │   └──     ...
     │
     └── Panoptic        
         ├── voc_panoptic_train_aug.json
         ├── voc_panoptic_val.json
         │
         ├── voc_panoptic_train_aug_1pnt_uniform
         │   ├── 20xx_xxxxxx.png
         │   └──     ...
         │
         └── voc_panoptic_val
             ├── 20xx_xxxxxx.png
             └──     ...
     

• MS COCO

  ...

Train & Test

Please find the example_run.sh for details.

TODO

• clean code
• update README.md