近日极市分享了一篇关于全景分割的综述：漫谈全景分割，今天分享一个全景分割相关资源列表，包含papers、code、 benchmark results等，可以结合文章一起阅读~

作者：Angzz
项目地址：Angzz/awesome-panoptic-segmentation

更多Awsome Github资源请关注：【Awsome】GitHub 资源汇总

Summarize in one sentence : Panoptic Segmentation proposes to solve the semantic segmentation(Stuff) and instance segmentation(Thing) in a unified and general manner.

Generally, the datasets which contains both semantic and instance annotations can be used to solve the challenging panoptic task.

• Cityscapes
• Mapillary Vistas
• ADE20K
• COCO-Panoptic
• BDD100K (the instance annotations are temporaily not released)

  Benchmark Results

• PQ are the standard metrics described in Panoptic Segmentation.
  

• PC are the standard metrics described in DeeperLab.
  
• COCO Benchmark

Method Backbone PQ PQ-Thing PQ-Stuff SQ RQ mIoU AP-Mask PC e2e AUNet ResNet-101 45.2 54.4 31.3 80.6 54.7 - - - UPSNet ResNet-101 42.5 48.6 33.4 - - 54.3 34.3 - OANet ResNet-101 41.3 50.4 27.7 - - - - - Panoptic FPN ResNet-101 40.9 48.3 29.7 - - - - - DeeperLab Xception-71 34.3 37.5 29.6 77.1 43.1 - - 56.8

• Cityscapes Benchmark

Method Backbone PQ PQ-Thing PQ-Stuff SQ RQ mIoU AP-Mask PC e2e Panoptic(Merge) - 61.2 66.4 54.0 80.9 74.4 - - - UPSNet ResNet-50 59.3 54.6 62.7 79.7 73.0 75.2 33.3 - TASCNet ResNet-101 59.2 56 61.5 - - 77.8 37.6 - Panoptic FPN ResNet-101 58.1 52.0 62.5 - - 75.7 33.0 - DeeperLab Xception-71 56.5 - - - - - - 75.6 AUNet ResNet-101 59.0 54.8 62.1 - - 75.6 34.4 -

• Mapillary Benchmark

Method Backbone PQ PQ-Thing PQ-Stuff SQ RQ mIoU AP-Mask PC e2e Panoptic(Merge) - 38.3 41.8 35.7 73.6 47.7 - - - TASCNet ResNet-101 32.6 31.3 34.4 - - 35.0 18.5 - DeeperLab Xception-71 31.6 25.0 40.3 75.5 40.1 - - 55.3

CVPR2019

• Panoptic Segmentation: Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, Piotr Dollár.
  "Panoptic Segmentation." CVPR (2019). [paper]

• Panoptic FPN: Alexander Kirillov, Ross Girshick, Kaiming He, Piotr Dollár.
  "Panoptic Feature Pyramid Networks." CVPR (2019 oral). [paper]

• AUNet: Yanwei Li, Xinze Chen, Zheng Zhu, Lingxi Xie, Guan Huang, Dalong Du, Xingang Wang.
  "Attention-guided Unified Network for Panoptic Segmentation." CVPR (2019). [paper]

• UPSNet: Yuwen Xiong, Renjie Liao, Hengshuang Zhao, Rui Hu, Min Bai, Ersin Yumer, Raquel Urtasun.
  "UPSNet: A Unified Panoptic Segmentation Network." CVPR (2019). [paper] [code]

• DeeperLab: Tien-Ju Yang, Maxwell D. Collins, Yukun Zhu, Jyh-Jing Hwang, Ting Liu, Xiao Zhang, Vivienne Sze, George Papandreou, Liang-Chieh Chen.
  "DeeperLab: Single-Shot Image Parser." CVPR (2019). [paper] [project] [code]

• TASCNet: Jie Li, Allan Raventos, Arjun Bhargava, Takaaki Tagawa, Adrien Gaidon.
  "Learning to Fuse Things and Stuff." CVPR (2019). [paper]

• OANet: Huanyu Liu, Chao Peng, Changqian Yu, Jingbo Wang, Xu Liu, Gang Yu, Wei Jiang.
  "An End-to-End Network for Panoptic Segmentation." CVPR (2019). [paper]

• Eirikur Agustsson, Jasper R. R. Uijlings, Vittorio Ferrari
  .
  "Interactive Full Image Segmentation by Considering All Regions Jointly." CVPR (2019). [paper]

ECCV2018

• Qizhu Li, Anurag Arnab, Philip H.S. Torr.
  "Weakly- and Semi-Supervised Panoptic Segmentation." ECCV (2018). [paper] [code]

  ArXiv

• Daan de Geus, Panagiotis Meletis, Gijs Dubbelman.
  "Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network." arXiv (2018). [paper]

• Daan de Geus, Panagiotis Meletis, Gijs Dubbelman.
  "Single Network Panoptic Segmentation for Street Scene Understanding." arXiv (2019). [paper]

• David Owen, Ping-Lin Chang.
  "Detecting Reflections by Combining Semantic and Instance Segmentation." arXiv (2019). [paper]

• Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji.
  "PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things." arXiv (2019). [paper]

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