全景分割研究综述

徐鹏斌; 瞿安国; 王坤峰; 李大字

doi:10.16383/j.aas.c200657

全景分割研究综述

doi: 10.16383/j.aas.c200657

1.
北京化工大学信息科学与技术学院北京 100029

基金项目: 国家自然科学基金(62076020, 61873022), 北京市自然科学基金(4182045)资助

详细信息

作者简介:
徐鹏斌：北京化工大学信息科学与技术学院硕士研究生. 2019年获得华北电力大学学士学位. 主要研究方向为深度学习, 计算机视觉, 图像全景分割. E-mail: 2019210488@mail.buct.edu.cn

瞿安国：北京化工大学信息科学与技术学院硕士研究生. 2018年获得北京理工大学学士学位. 主要研究方向为深度学习, 计算机视觉, 图像全景分割. E-mail: 2018210472@mail.buct.edu.cn

王坤峰：北京化工大学信息科学与技术学院教授. 主要研究方向为计算机视觉, 机器学习, 智能无人系统. 本文通信作者. E-mail: wangkf@mail.buct.edu.cn

李大字：北京化工大学信息科学与技术学院教授. 主要研究方向为人工智能, 先进控制, 分数阶系统, 复杂系统建模与优化. E-mail: lidz@mail.buct.edu.cn

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出版历程
- 收稿日期: 2020-08-15
- 录用日期: 2020-10-10
- 网络出版日期: 2020-12-28
- 刊出日期: 2021-04-02

A Survey of Panoptic Segmentation Methods

1.
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029

Funds: Supported by National Natural Science Foundation of China (62076020, 61873022) and Beijing Municipal Natural Science Foundation (4182045)

More Information

Author Bio:
XU Peng-Bin　Master student at the College of Information Science and Technology, Beijing University of Chemical Technology. He received his bachelor degree from North China Electric Power University in 2019. His research interest covers deep learning, computer vision, and panoptic segmentation

QU An-Guo　Master student at the College of Information Science and Technology, Beijing University of Chemical Technology. He received his bachelor degree from Beijing Institute of Technology in 2018. His research interest covers deep learning, computer vision, and panoptic segmentation

WANG Kun-Feng　Professor at the College of Information Science and Technology, Beijing University of Chemical Technology. His research interest covers computer vision, machine learning, and intelligent unmanned systems. Corresponding author of this paper

LI Da-Zi　Professor at the College of Information Science and Technology, Beijing University of Chemical Technology. Her research interest covers artificial intelligence, advanced control, fractional order systems, and complex system modeling and optimization

摘要

摘要:
在计算机视觉领域, 全景分割是一个新颖且重要的研究主题, 它是机器感知、自动驾驶等新兴前沿技术的基石, 具有十分重要的研究意义. 本文综述了基于深度学习的全景分割研究的最新进展, 首先总结了全景分割任务的基本处理流程, 然后对已发表的全景分割工作基于其网络结构特点进行分类, 并进行了全面的介绍与分析, 最后对全景分割任务目前面临的问题以及未来的发展趋势做出了分析, 并针对所面临的问题提出了一些切实可行的解决思路.
- 全景分割 /
- 语义分割 /
- 实例分割 /
- 深度学习
Abstract:
In the field of computer vision, panoptic segmentation is a novel and important research topic. It is the cornerstone of emerging leading-edge technologies such as machine perception and autonomous driving, and has very important significance of research. This paper reviews the research progress of panoptic segmentation based on deep learning methods, summarizes the basic processing flow of panoptic segmentation, and then classifies the already published panoptic segmentation works based on the characteristics of its network structure, and makes a more comprehensive introduction and analysis. Finally, the current problems and future development trends of the panoptic segmentation task are analyzed, and some potential solutions are provided for solving the existing problems.
- Panoptic segmentation /
- semantic segmentation /
- instance segmentation /
- deep learning

HTML全文

图 1 全景分割流程图

Fig. 1 The processing flow of panoptic segmentation

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图 2 LeNet-5的网络结构^[31]

Fig. 2 The structure of LeNet-5^[31]

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图 3 VGG-16的网络结构

Fig. 3 The structure of VGG-16

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图 4 ResNet网络的残差模块^[15]

Fig. 4 The residual module of ResNet^[15]

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图 5 语义分割处理流程

Fig. 5 The processing flow of semantic segmentation

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图 6 BlitzNet网络的基本结构^[50]

Fig. 6 The structure of BlitzNet^[50]

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图 7 DeeperLab全景分割结构^[52]

Fig. 7 The structure of DeeperLab panoptic segmentation^[52]

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图 8 Panoptic-DeepLab模型结构^[57]

Fig. 8 The structure of Panoptic-DeepLab model^[57]

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图 9 BBFNet模型结构^[58]

Fig. 9 The structure of BBFNet model^[58]

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图 10 PCV模型结构^[63]

Fig. 10 The structure of PCV model^[63]

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图 11 轴注意力模型结构^[64]

Fig. 11 The structure of axial-attention model^[64]

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图 12 TASCNet模型结构^[10]

Fig. 12 The structure of TASCNet model^[10]

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图 13 学习实例遮挡网络结构^[68]

Fig. 13 The structure of learning instance occlusion for panoptic segmentation^[68]

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图 14 SOGNet模型结构^[29]

Fig. 14 The structure of SOGNet model^[29]

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图 15 BCRF网络结构^[17]

Fig. 15 The structure of BCRF network^[17]

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图 16 EfficientPS模型结构^[76]

Fig. 16 The structure of EfficientPS model^[76]

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图 17 BANet模型结构^[77]

Fig. 17 The structure of BANet model^[77]

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图 18 PanopticTrackNet模型结构^[78]

Fig. 18 The structure of PanopticTrackNet model^[78]

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表 1 现有单阶段方法性能比较

Table 1 Performance comparison of existing single-stage methods

模型	数据集	PQ	mIoU	AP	mAP	Inference time
BlitzNet^[50]	PASCAL VOC	—	—	—	83.8	24 帧/s
DeeperLab^[52]	Mapillary Vistas validation set	31.95	—	—	—	—
Generator evaluator-selector net^[53]	MS COCO	33.7	—	—	—	—
FPSNet^[22]	CityScapes validation set	55.1	—	—	—	114 ms
SpatialFlow^[55]	MS COCO 2017 test-dev split	47.3	—	—	36.7	—
Single-shot panoptic segmentation^[28]	MS COCO val2017	32.4	27.9	33.1	—	21.8 帧/s
Panoptic-DeepLab^[57]	MS COCO val set	39.7	—	—	—	132 ms
Real-time panoptic segmentation from dense detections^[56]	MS COCO val set	37.1	—	—	—	63 ms
BBFNet^[58]	MS COCO-2017 dataset	37.1	—	—	—	—
Axial-DeepLab^[64]	Cityscapes test set	62.8	79.9	34.0	—	—
EPSNet^[62]	MS COCO val set	38.6	—	—	—	53 ms
PCV^[63]	Cityscapes val set	54.2	74.1	—	—	182.8 ms

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表 2 现有两阶段方法性能对比

Table 2 Performance comparison of existing two-stage methods

模型	数据集	PQ	mIoU	AP	mAP	Inference time (ms)
Weakly- and semi-supervised panoptic segmentation^[16]	VOC 2012 validation set	63.1	—	59.5	—	—
JSIS-Net^[9]	MS COCO test-dev	27.2	—	—	—	—
TASCNet^[10]	Cityscapes	60.4	78.7	39.09	—	—
AUNet^[11]	Cityscapes val set	59.0	75.6	34.4	—	—
Panoptic feature pyramid networks^[26]	MS COCO test-dev	40.9	—	—	—	—
UPSNet^[27]	MS COCO	42.5	54.3	34.3	—	17
Single network panoptic segmentation for street scene understanding^[12]	Mapillary Vistas	23.9	—	—	—	484
OANet^[13]	MS COCO 2018 panoptic segmentation challenge test-dev	41.3	—	—	—	—
OCFusion^[68]	MS COCO test-dev dataset	46.7	—	—	—	—
SOGNet^[29]	MS COCO	43.7	—	—	—	—
PanDA^[69]	MS COCO subsets	37.4	45.9	28.0	—	—
BCRF^[17]	Pascal VOC dataset	71.76	—	—	—	—
Unifying training and inference for panoptic segmentation^[70]	MS COCO test-dev set	47.2	—	—	—	—
BANet^[77]	MS COCO val set	41.1	—	—	—	—
EfficientPS^[76]	Cityscapes validation set	63.6	79.3	37.4	—	166

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参考文献(80)

[1]	Kirillov A, He K M, Girshick R, Rother C, Dollar P. Panoptic segmentation. In: Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA, USA: IEEE, 2019. 9396−9405
[2]	胡涛, 李卫华, 秦先祥. 图像语义分割方法综述. 测控技术, 2019, 38(7): 8−12 Hu Tao, Li Wei-Hua, Qin Xian-Xiang. A review on image semantic segmentation. Measurement and Control Technology, 2019, 38(7): 8−12
[3]	Yang Y, Hallman S, Ramanan D, Fowlkes C C. Layered object models for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(9): 1731−1743 doi: 10.1109/TPAMI.2011.208
[4]	Ladicky L, Russell C, Kohli P, Torr P H S. Associative hierarchical CRFs for object class image segmentation. In: Proceedings of the 12th IEEE International Conference on Computer Vision. Kyoto, Japan: IEEE, 2009. 739−746
[5]	Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640−651 doi: 10.1109/TPAMI.2016.2572683
[6]	Hariharan B, Arbelaez P, Girshick R, Malik J. Simultaneous detection and segmentation. In: European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014. 297−312
[7]	Pinheiro P O, Collobert R, Dollar P. Learning to segment object candidates. In: Proceedings of the 28th International Conference on Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2015. 1990−1998
[8]	Zagoruyko S, Lerer A, Lin T Y, Pinheiro P O, Gross S, Chintala S, et al. A MultiPath network for object detection. [Online], available: https://arxiv.org/abs/1604.02135, April 4, 2016
[9]	De Geus D, Meletis P, Dubbelman G. Panoptic segmentation with a joint semantic and instance segmentation network. [Online], available: https://arxiv.org/abs/1809.02110, September 6, 2018
[10]	Li J, Raventos A, Bhargava A, Tagawa T, Gaidon A. Learning to fuse things and stuff. [Online], available: https://arxiv.org/abs/1812.01192, December 4, 2018
[11]	Li Y W, Chen X Z, Zhu Z, Xie L X, Huang G, Du D L, et al. Attention-guided unified network for panoptic segmentation. In: Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA, USA: IEEE, 2019. 7019−7028
[12]	De Geus D, Meletis P, Dubbelman G. Single network panoptic segmentation for street scene understanding. In: Proceedings of IEEE Intelligent Vehicles Symposium (IV). Paris, France: IEEE, 2019. 709−715
[13]	Liu H Y, Peng C, Yu C Q, Wang J B, Liu X, Yu G, et al. An end-to-end network for panoptic segmentation. In: Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA, USA: IEEE, 2019. 6165−6174
[14]	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. [Online], available: https://arxiv.org/abs/1409.1556, September 4, 2014
[15]	He K M, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016. 770−778
[16]	Li Q Z, Arnab A, Torr P H S. Weakly- and semi-supervised panoptic segmentation. In: Proceedings of the 2018 European Conference on Computer Vision. Munich, Germany: Springer, 2018. 106−124
[17]	Jayasumana S, Ranasinghe K, Jayawardhana M, Liyanaarachchi S, Ranasinghe H. Bipartite conditional random fields for panoptic segmentation. [Online], available: https://arxiv.org/abs/1912.05307, December 11, 2019
[18]	Huang G, Liu Z, Van Der Maaten L, Weinberger K Q. Densely connected convolutional networks. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017. 2261−2269
[19]	Howard A G, Zhu M L, Chen B, Kalenichenko D, Wang W J, Weyand T, et al. MobileNets: Efficient convolutional neural networks for mobile vision applications. [Online], available: https://arxiv.org/abs/1704.04861, April 17, 2017
[20]	Sandler M, Howard A, Zhu M L, Zhmoginov A, Chen L C. Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018. 4510−4520
[21]	Howard A, Sandler M, Chen B, Wang W J, Chen L C, Tang M X, et al. Searching for MobileNetV3. In: Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE, 2019. 1314−1324
[22]	De Geus D, Meletis P, Dubbelman G. Fast panoptic segmentation network. IEEE Robotics and Automation Letters, 2020, 5(2): 1742−1749
[23]	肖晓伟, 肖迪, 林锦国, 肖玉峰. 多目标优化问题的研究概述. 计算机应用研究, 2011, 28(3): 805−808 Xiao Xiao-Wei, Xiao Di, Lin Jin-Guo, Xiao Yu-Feng. Overview on multi-objective optimization problem research. Application Research of Computers, 2011, 28(3): 805−808
[24]	葛继科, 邱玉辉, 吴春明, 蒲国林. 遗传算法研究综述. 计算机应用研究, 2008, 25(10): 2911−2916 Ge Ji-Ke, Qiu Yu-Hui, Wu Chun-Ming, Pu Guo-Lin. Summary of genetic algorithms research. Application Research of Computers, 2008, 25(10): 2911−2916
[25]	巫影, 陈定方, 唐小兵, 朱石坚, 黄映云, 李庆. 神经网络综述. 科技进步与对策, 2002, 19(6): 133−134 Wu Ying, Chen Ding-Fang, Tang Xiao-Bing, Zhu Shi-Jian, Huang Ying-Yun, Li Qing. Summarizing of neural network. Science & Technology Progress and Policy, 2002, 19(6): 133−134
[26]	Kirillov A, Girshick R, He K M, Dollar P. Panoptic feature pyramid networks. In: Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA, USA: IEEE, 2019. 6392−6401
[27]	Xiong Y W, Liao R J, Zhao H S, Hu R, Bai M, Yumer E, et al. UPSNet: A unified panoptic segmentation network. In: Proceedings of the 2019 IEEE/CVF Conference on Computer Vision andPattern Recognition. Long Beach, CA, USA: IEEE, 2019. 8810−8818
[28]	Weber M, Luiten J, Leibe B. Single-shot panoptic segmentation. [Online], available: https://arxiv.org/abs/1911.00764, November 2, 2019
[29]	Yang Y B, Li H Y, Li X, Zhao Q J, Wu J L, Lin Z C. SOGNet: Scene overlap graph network for panoptic segmentation, Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 12637−12644
[30]	Fukushima K. Neocognitron: A self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 1980, 36(4): 193−202 doi: 10.1007/BF00344251
[31]	LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11): 2278−2324 doi: 10.1109/5.726791
[32]	Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 2017, 60(6): 84−90 doi: 10.1145/3065386
[33]	Szegedy C, Liu W, Jia Y Q, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE, 2015. 1−9
[34]	Chen L C, Papandreou G, Kokkinos I, Murphy K, Yuille A L. Semantic image segmentation with deep convolutional nets and fully connected CRFs. [Online], available: https://arxiv.org/abs/1412.7062, December 22, 2014
[35]	Chen L C, Papandreou G, Kokkinos I, Murphy K, Yuille A L. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834−848 doi: 10.1109/TPAMI.2017.2699184
[36]	Chen L C, Hermans A, Papandreou G, Schroff F, Wang P, Adam H. MaskLab: Instance segmentation by refining object detection with semantic and direction features. In: Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018. 4013−4022
[37]	Ronneberger O, Fischer P, Brox T, et al. U-Net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention. Munich, Germany: Springer, 2015. 234−241
[38]	Zhao H S, Shi J P, Qi X J, Wang X G, Jia J Y. Pyramid scene parsing network. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017. 6230−6239
[39]	He K M, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. In: Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2017. 2980−2988
[40]	Ren S Q, He K M, Girshick R, Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 39(6): 1137−1149
[41]	Liu S, Qi L, Qin H F, Shi J P, Jia J Y. Path aggregation network for instance segmentation. In: Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018. 8759−8768
[42]	Zhang H, Tian Y L, Wang K F, Zhang W S, Wang F Y. Mask SSD: An effective single-stage approach to object instance segmentation. IEEE Transactions on Image Processing, 2019, 29: 2078−2093
[43]	Everingham M, Eslami S M, Van Gool L, Williams C K I, Winn J, Zisserman A. The PASCAL visual object classes challenge: A retrospective. International Journal of Computer Vision, 2015, 111(1): 98−136 doi: 10.1007/s11263-014-0733-5
[44]	Lin T T, Maire M, Belongie S, Hays J, Perona P, Ramanan D, et al. Microsoft COCO: Common objects in context. In: European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014. 740−755
[45]	Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, et al. The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016. 3213−3223
[46]	Zhou B L, Zhao H, Puig X, Fidler S, Barriuso A, Torralba A. Scene parsing through ADE20K dataset. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017. 5122−5130
[47]	Neuhold G, Ollmann T, Bulo S R, Kontschieder P. The Mapillary vistas dataset for semantic understanding of street scenes. In: Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2017. 5000−5009
[48]	Garcia-Garcia A, Orts-Escolano S, Oprea S, Villena-Martinez V, Garcia-Rodriguez J. A review on deep learning techniques applied to semantic segmentation. [Online], available: https://arxiv.org/abs/1704.06857, April 22, 2017
[49]	Lateef F, Ruichek Y. Survey on semantic segmentation using deep learning techniques. Neurocomputing, 2019, 338: 321−348 doi: 10.1016/j.neucom.2019.02.003
[50]	Dvornik N, Shmelkov K, Mairal J, Schmid C. BlitzNet: A real-time deep network for scene understanding. In: Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2017. 4174−4182
[51]	Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C Y, et al. SSD: Single shot MultiBox detector. In: European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016. 21−37
[52]	Yang T J, Collins M D, Zhu Y K, Hwang J J, Liu T, Zhang X, et al. DeeperLab: Single-shot image parser. [Online], available: https://arxiv.org/abs/1902.05093, February 13, 2019
[53]	Eppel S, Aspuru-Guzik A. Generator evaluator-selector net: A modular approach for panoptic segmentation. [Online], available: https://arxiv.org/abs/1908.09108, August 24, 2019
[54]	Eppel S. Class-independent sequential full image segmentation, using a convolutional net that finds a segment within an attention region, given a pointer pixel within this segment. [Online], available: https://arxiv.org/abs/1902.07810, February 20, 2019
[55]	Chen Q, Cheng A D, He X Y, Wang P S, Cheng J. SpatialFlow: Bridging all tasks for panoptic segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 2020.
[56]	Hou R, Li J, Bhargava A, Raventos A, Guizilini V, Fang C, et al. Real-time panoptic segmentation from dense detections. In: Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA, USA: IEEE, 2020. 8520−8529
[57]	Cheng B W, Collins M D, Zhu Y K, Liu T, Huang T S, Adam H, et al. Panoptic-DeepLab. [Online], available: https://arxiv.org/abs/1910.04751, October 24, 2019
[58]	Bonde U, Alcantarilla P F, Leutenegger S. Towards bounding-box free panoptic segmentation. [Online], available: https://arxiv.org/abs/2002.07705, February 18, 2020
[59]	Dai J F, Qi H Z, Xiong Y W, Li Y, Zhang G D, Hu H, et al. Deformable convolutional networks. In: Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2017. 764−773
[60]	Ballard D H. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition, 1981, 13(2): 111−122 doi: 10.1016/0031-3203(81)90009-1
[61]	Bai M, Urtasun R. Deep watershed transform for instance segmentation. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017. 2858−2866
[62]	Chang C Y, Chang S E, Hsiao P Y, Fu L C. EPSNet: Efficient panoptic segmentation network with cross-layer attention fusion. [Online], available: https://arxiv.org/abs/2003.10142, March 23, 2020
[63]	Wang H C, Luo R T, Maire M, Shakhnarovich G. Pixel consensus voting for panoptic segmentation. In: Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA, USA: IEEE, 2020. 9461−9470
[64]	Wang H Y, Zhu Y K, Green B, Adam H, Yuille A, Chen L C. Axial-DeepLab: Stand-alone axial-attention for panoptic segmentation. [Online], available: https://arxiv.org/abs/2003.07853, March 17, 2020
[65]	Arnab A, Torr P H S. Pixelwise instance segmentation with a dynamically instantiated network. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017. 879−888
[66]	Rother C, Kolmogorov V, Blake A. “GrabCut”: Interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics, 2004, 23(3): 309−314 doi: 10.1145/1015706.1015720
[67]	Arbelaez P, Pont-Tuset J, Barron J, Marques F, Malik J. Multiscale combinatorial grouping. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA: IEEE, 2014. 328−335
[68]	Lazarow J, Lee K, Shi K Y, Tu Z W. Learning instance occlusion for panoptic segmentation. In: Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA, USA: IEEE, 2020. 10717−10726
[69]	Liu Y, Perona P, Meister M. PanDA: Panoptic data augmentation. Liu Y, Perona P, Meister M. PanDA: Panoptic data augmentation. [Online], available: https://arxiv.org/abs/1911.12317, November 27, 2019
[70]	Li Q Z, Qi X J, Torr P H S. Unifying training and inference for panoptic segmentation. In: Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA, USA: IEEE, 2020. 13317−13325
[71]	Behley J, Milioto A, Stachniss C. A benchmark for LiDAR-based panoptic segmentation based on KITTI. [Online], available: https://arxiv.org/abs/2003.02371, March 4, 2020
[72]	Behley J, Garbade M, Milioto A, Quenzel J, Behnke S, Stachniss C, et al. SemanticKITTI: A dataset for semantic scene understanding of LiDAR sequences. In: Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE, 2019. 9296−9306
[73]	Thomas H, Qi C R, Deschaud J E, Marcotegui B, Goulette F, Guibas L. KPConv: Flexible and deformable convolution for point clouds. In: Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE, 2019. 6410−6419
[74]	Milioto A, Vizzo I, Behley J, Stachniss C. RangeNet++: Fast and accurate LiDAR semantic segmentation. In: Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Macau, China: IEEE, 2019. 4213−4220
[75]	Lang A H, Vora S, Caesar H, Zhou L B, Yang J, Beijbom O. PointPillars: Fast encoders for object detection from point clouds. In: Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA, USA: IEEE, 2019. 12689−12697
[76]	Mohan R, Valada A. EfficientPS: Efficient panoptic segmentation. [Online], available: https://arxiv.org/abs/2004.02307, April 5, 2020
[77]	Chen Y F, Lin G C, Li S Y, Bourahla O, Wu Y M, Wang F F, et al. BANet: Bidirectional aggregation network with occlusion handling for panoptic segmentation. In: Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA, USA: IEEE, 2020. 3792−3801
[78]	Hurtado J V, Mohan R, Burgard W, Valada A. MOPT: Multi-object panoptic tracking. [Online], available: https://arxiv.org/abs/2004.08189, April 17, 2020
[79]	张慧, 王坤峰, 王飞跃. 深度学习在目标视觉检测中的应用进展与展望. 自动化学报, 2017, 43(8): 1289−1305 Zhang Hui, Wang Kun-Feng, Wang Fei-Yue. Advances and perspectives on applications of deep learning in visual object detection. Acta Automatica Sinica, 2017, 43(8): 1289−1305
[80]	Meletis P, Wen X X, Lu C Y, de Geus D, Dubbelman G. Cityscapes-panoptic-parts and PASCAL-panoptic-parts datasets for scene understanding. [Online], available: https://arxiv.org/abs/2004.07944, April 16, 2020