2024, Volume 26, Issue 1
Strategic Study of CAE >> 2024, Volume 26, Issue 1 doi: 10.15302/J-SSCAE-2024.01.017
Privacy Protection of Sensitive Bioinformation Based on Event Cameras
1. National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an 710049, China;
2. National Engineering Research Center for Visual Information and Applications, Xi’an 710049, China;
3. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, China
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Abstract
进入大数据时代后，互联网应用和信息服务全面普及，大量的个人敏感生物信息被收集整理，导致隐私泄露风险增加；事件相机作为新型的生物启发式传感器，具有低延迟、高动态、无纹理等特性，可为解决数据端隐私保护问题提供全新的技术途径，也因其光敏工作原理而适用于家庭监控等私人场景。本文从大数据时代的隐私泄露问题、事件相机在隐私保护中的优势两方面，深入分析了事件相机用于隐私保护的研究背景；系统梳理了基于人脸模板的隐私保护、基于去识别的隐私保护、基于点云混沌加密的隐私保护等传统的敏感生物信息隐私保护方法，包括行人重识别、手势识别、面部分析在内的面向隐私保护的事件感知方法以及两大类方法的研究进展。进一步总结了强度图像重建、图像修复、视频重建等基于事件流的图像重建与修复新进展，完成了基于6 种算法的图像重建及其结果分析，证明已有重建算法对纹理信息的恢复能力有限，反向验证了基于事件相机的隐私保护技术可行性。针对事件相机未来的规模化应用，提出了降低硬件成本、改进算法网络、从市场角度推动等发展建议，以期为事件相机的隐私保护深化应用提供基础参考。
Keywords
intelligent system ; event camera ; privacy protection ; sensitive bioinformation
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References
[ 1 ] Liu B, Ding M, Shaham S, et al. When machine learning meets privacy: A survey and outlook [J]. ACM Computing Surveys, 54(2): 31.
[ 2 ] Ahmad S, Morerio P, Bue A D. Person re-identification without identification via event anonymization [EB/OL]. (2023-08-08)[2023-11-15]. https: //arxiv.org/abs/2308.04402.
[ 3 ] Gallego G, Delbrück T, Orchard G, et al. Event-based vision: A survey [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(1): 154‒180.
[ 4 ]
徐齐, 邓洁, 申江荣, 等. 基于事件相机的图像重构综述 [J]. 电子与信息学报, 2023, 45(8): 2699‒2709.
Xu Q, Deng J, Shen J R, et al. A review of image reconstruction based on event cameras [J]. Journal of Electronics & Information Technology, 2023, 45(8): 2699‒2709.
[ 5 ] Terhorst P, Fahrmann D, Damer N, et al. Beyond identity: What information is stored in biometric face templates? [C]. Houston: 2020 IEEE International Joint Conference on Biometrics (IJCB), 2020.
[ 6 ] Feutry C, Piantanida P, Bengio Y, et al. Learning anonymized representations with adversarial neural networks [EB/OL]. (2018-02-26)[2023-11-15]. https: //arxiv.org/abs/1802.09386.
[ 7 ] Li J Z, Zhang H, Liang S Y, et al. Privacy-enhancing face obfuscation guided by semantic-aware attribution maps [J]. IEEE Transactions on Information Forensics and Security, 2023, 18: 3632‒3646.
[ 8 ] Mirjalili V, Raschka S, Namboodiri A, et al. Semi-adversarial networks: Convolutional autoencoders for imparting privacy to face images [C]. Gold Coast: 2018 International Conference on Biometrics (ICB), 2018.
[ 9 ] Mirjalili V, Ross A. Soft biometric privacy: Retaining biometric utility of face images while perturbing gender [C]. Denver: 2017 IEEE International Joint Conference on Biometrics (IJCB), 2017.
[10] Morales A, Fierrez J, Vera-Rodriguez R, et al. SensitiveNets: Learning agnostic representations with application to face images [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(6): 2158‒2164.
[11] Terhörst P, Damer N, Kirchbuchner F, et al. Unsupervised privacy-enhancement of face representations using similarity-sensitive noise transformations [J]. Applied Intelligence, 2019, 49(8): 3043‒ 3060.
[12] Terhorst P, Huber M, Damer N, et al. Unsupervised enhancement of soft-biometric privacy with negative face recognition [EB/OL]. (2020-02-21)[2023-11-15]. https: //arxiv.org/abs/2002.09181.
[13] Terhorst P, Riehl K, Damer N, et al. PE-MIU: A training-free privacy-enhancing face recognition approach based on minimum information units [J]. IEEE Access, 2020, 8: 93635‒93647.
[14] Porikli F, Bremond F, Dockstader S L, et al. Video surveillance: Past, present, and now the future [DSP Forum] [J]. IEEE Signal Processing Magazine, 2013, 30(3): 190‒198.
[15] Slobogin C. Public privacy: Camera surveillance of public places and the right to anonymity [EB/OL]. (2023-02-24)[2023-11-15]. https: //api.semanticscholar.org/CorpusID: 150761588.
[16] Winkler T, Rinner B. TrustCAM: Security and privacy-protection for an embedded smart camera based on trusted computing [C]. Boston: 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2010.
[17] Mrityunjay N P J. The de-identification camera [C]. Hubli: 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, 2011.
[18] Zhang Y P, Lu Y H, Nagahara H, et al. Anonymous camera for privacy protection [C]. Stockholm: 2014 22nd International Conference on Pattern Recognition, 2014.
[19] Bai B J, Luo Y, Gan T Y, et al. To image, or not to image: Class-specific diffractive cameras with all-optical erasure of undesired objects [J]. eLight, 2022, 2(1): 14.
[20]
刘昭辛, 吴金建, 石光明, 等. 面向事件相机的轻量化脉冲识别网络 [J]. 中国科学: 信息科学, 2023, 53(7): 1333‒1347.
Liu Z X, Wu J J, Shi G M, et al. Towards event camera signal recognition using a lightweight spiking neural network [J]. SCIENTIA SINICA Informationis, 2023, 53(7): 1333‒1347.
[21] Deng L, Wu Y J, Hu Y F, et al. Comprehensive SNN compression using ADMM optimization and activity regularization [J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(6): 2791‒2805.
[22] Zhang T L, Jia S C, Cheng X, et al. Tuning convolutional spiking neural network with biologically plausible reward propagation [J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(12): 7621‒7631.
[23] Korshunov P, Ebrahimi T. Using warping for privacy protection in video surveillance [C]. Fira: 2013 18th International Conference on Digital Signal Processing (DSP), 2013.
[24] Yuan L, Ebrahimi T. Image privacy protection with secure JPEG transmorphing [J]. IET Signal Processing, 2017, 11(9): 1031‒1038.
[25] Newton E M, Sweeney L, Malin B. Preserving privacy by de-identifying face images [J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(2): 232‒243.
[26] Bitouk D, Kumar N, Dhillon S, et al. Face swapping: Automatically replacing faces in photographs [J]. ACM Transactions on Graphics, 27(3): 1‒8.
[27] Brkic K, Sikiric I, Hrkac T, et al. I know that person: Generative full body and face de-identification of people in images [C]. Honolulu: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017.
[28] Chi H H, Hu Y H. Face de-identification using facial identity preserving features [C]. Orlando: 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015.
[29] Mosaddegh S, Simon L, Jurie F. Photorealistic face de-identification by aggregating donors´ face components [C]. Singapore: Computer Vision-ACCV 2014, 2014.
[30] Zhai L M, Guo Q, Xie X F, et al. A3GAN: Attribute-aware anonymization networks for face de-identification [C]. Lisboa: Proceedings of the 30th ACM International Conference on Multimedia, 2022.
[31] Erdelyi A, Barat T, Valet P, et al. Adaptive cartooning for privacy protection in camera networks [C]. Seoul: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2014.
[32] Letournel G, Bugeau A, Ta V T, et al. Face de-identification with expressions preservation [C]. Quebec City: 2015 IEEE International Conference on Image Processing (ICIP), 2015.
[33] Jin X, Wu Z, Song C, et al. 3D point cloud encryption through chaotic mapping [C]. Xi´an: Advances in Multimedia Information Processing-PCM 2016, 2016.
[34] Annaby M H, Mahmoud M E, Abdusalam H A, et al. Geometrically stable 3D-point cloud encryption via chaotic permutations and pointwise rotations [EB/OL]. (2023-05-18)[2023-11-15]. https: //www.researchsquare.com/article/rs-2940407/v1.
[35] Jia C C, Yang T, Wang C J, et al. Encryption of 3D point cloud using chaotic cat mapping [J]. 3D Research, 2019, 10(1): 4.
[36] Liu B C, Liu Y X, Xie Y Y, et al. Privacy protection for 3D point cloud classification based on an optical chaotic encryption scheme [J]. Optics Express, 2023, 31(5): 8820‒8843.
[37] Jin X, Zhu S Y, Wu L, et al. Multi-level chaotic maps for 3D textured model encryption [C]. Kitakyushu: 2nd EAI International Conference on Robotic Sensor Networks, 2019.
[38] Jolfaei A, Wu X W, Muthukkumarasamy V. A 3D object encryption scheme which maintains dimensional and spatial stability [J]. IEEE Transactions on Information Forensics and Security, 2015, 10(2): 409‒422.
[39] Priyadarsini K, Kumar Sivaraman A, Quadir Md A, et al. Securing 3D point and mesh fog data using novel chaotic cat map [J]. Computers, Materials & Continua, 2023, 74(3): 6703‒6717.
[40] Rajakumar M P, Ramya J, Sonia R. A novel scheme for encryption and decryption of 3D point and mesh cloud data in cloud computing [J]. Journal of Control Engineering and Applied Informatics, 2021, 23(1): 93‒102.
[41]
吴肇星, 金鑫, 宋承根, 等. 基于随机可逆矩阵的3D点云模型加密 [J]. 系统仿真学报, 2016, 28(10): 2455‒2459.
Wu Z X, Jin X, Song C G, et al. Random reversible matrix based point cloud encryption [J]. Journal of System Simulation, 2016, 28(10): 2455‒2459.
[42] Wang Y X, Du B W, Shen Y R, et al. EV-gait: Event-based robust gait recognition using dynamic vision sensors [C]. Long Beach: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
[43] Sokolova A, Konushin A. Human identification by gait from event-based camera [C]. Tokyo: 2019 16th International Conference on Machine Vision Applications (MVA), 2019.
[44] Wang Y X, Zhang X, Shen Y R, et al. Event-stream representation for human gaits identification using deep neural networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(7): 3436‒3449.
[45] Ahmad S, Scarpellini G, Morerio P, et al. Event-driven re-id: A new benchmark and method towards privacy-preserving person re-identification [C]. Waikoloa: 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), 2022.
[46] Moreira G, Graca A, Silva B, et al. Neuromorphic event-based face identity recognition [C]. Montreal: 2022 26th International Conference on Pattern Recognition (ICPR), 2022.
[47] Chen G, Wang F, Yuan X D, et al. NeuroBiometric: An eye blink based biometric authentication system using an event-based neuromorphic vision sensor [J]. CAA Journal of Automatica Sinica, 2021, 8(1): 206‒218.
[48] Ahn E Y, Lee J H, Mullen T, et al. Dynamic vision sensor camera based bare hand gesture recognition [C]. Paris: 2011 IEEE Symposium on Computational Intelligence For Multimedia, Signal and Vision Processing, 2011.
[49] Grossberg S. Adaptive resonance theory: How a brain learns to consciously attend, learn, and recognize a changing world [J]. Neural Networks, 2013, 37: 1‒47.
[50] Lee J H, Delbruck T, Pfeiffer M, et al. Real-time gesture interface based on event-driven processing from stereo silicon retinas [J]. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(12): 2250‒2263.
[51] Park P K J, Lee J H, Shin C W, et al. Gesture recognition system based on adaptive resonance theory [C]. Tsukuba: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), 2012.
[52] Park P K J, Lee K, Lee J H, et al. Computationally efficient, real-time motion recognition based on bio-inspired visual and cognitive processing [C]. Quebec City: IEEE International Conference on Image Processing, 2015.
[53] Xu Q, Li Y X, Shen J R, et al. Hierarchical spiking-based model for efficient image classification with enhanced feature extraction and encoding [J]. IEEE Transactions on Neural Networks and Learning Systems, 2022 (1): 1‒9.
[54] Xu Q, Li Y X, Shen J R, et al. Constructing deep spiking neural networks from artificial neural networks with knowledge distillation [C]. Vancouver: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
[55]
李雅馨, 申江荣, 徐齐. 面向图像识别的多层脉冲神经网络学习算法综述 [J]. 中国图象图形学报, 2023, 28(2): 385‒400.
Li Y X, Shen J R, Xu Q. A summary of image recognition-relevant multi-layer spiking neural networks learning algorithms [J]. Journal of Image and Graphics, 2023, 28(2): 385‒400.
[56] Xing Y N, Caterina G D, Soraghan J. A new spiking convolutional recurrent neural network (SCRNN) with applications to event-based hand gesture recognition [J]. Frontiers in Neuroscience, 2020, 14: 590164.
[57] Park P K J, Cho B H, Park J M, et al. Performance improvement of deep learning based gesture recognition using spatiotemporal demosaicing technique [C]. Phoenix: 2016 IEEE International Conference on Image Processing (ICIP), 2016.
[58] Wang Q Y, Zhang Y X, Yuan J S, et al. Space-time event clouds for gesture recognition: From RGB cameras to event cameras [C]. Waikoloa: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 2019.
[59] Chen J M, Meng J J, Wang X C, et al. Dynamic graph CNN for event-camera based gesture recognition [C]. Seville: 2020 IEEE International Symposium on Circuits and Systems (ISCAS), 2020.
[60] Chen G, Chen J N, Lienen M, et al. FLGR: Fixed length gists representation learning for RNN-HMM hybrid-based neuromorphic continuous gesture recognition [J]. Frontiers in Neuroscience, 2019, 13: 73.
[61] Sabater A, Montesano L, Murillo A C. Event Transformer. A sparse-aware solution for efficient event data processing [C]. New Orleans: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022.
[62] Amir A, Taba B, Berg D, et al. A low power, fully event-based gesture recognition system [C]. Honolulu: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
[63] Maro J M, Ieng S H, Benosman R. Event-based gesture recognition with dynamic background suppression using smartphone computational capabilities [J]. Frontiers in Neuroscience, 2020, 14: 275.
[64] Vasudevan A, Negri P, Linares-Barranco B, et al. Introduction and analysis of an event-based sign language dataset [C]. Buenos Aires: 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), 2020.
[65] Vasudevan A, Negri P, Di Ielsi C, et al. SL-Animals-DVS: Event-driven sign language animals dataset [J]. Pattern Analysis and Applications, 2022, 25(3): 505‒520.
[66] Chen X N, Su L, Zhao J X, et al. Sign language gesture recognition and classification based on event camera with spiking neural networks [J]. Electronics, 2023, 12(4): 786.
[67] Shi Q, Ye Z F, Wang J, et al. QISampling: An effective sampling strategy for event-based sign language recognition [J]. IEEE Signal Processing Letters, 2023, 30: 768‒772.
[68] Angelopoulos A N, Martel J N P, Wetzstein G. Event-based, near-eye gaze tracking beyond 10, 000 Hz [J]. IEEE Transactions on Visualization and Computer Graphics, 2021, 27(5): 2577‒2586.
[69] Stoffregen T, Daraei H, Robinson C, et al. Event-based kilohertz eye tracking using coded differential lighting [C]. Waikoloa: 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022.
[70] Li N, Bhat A, Raychowdhury A. E-track: Eye tracking with event camera for extended reality (XR) applications [C]. Hangzhou: 2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2023.
[71] Chen G, Hong L, Dong J H, et al. EDDD: Event-based drowsiness driving detection through facial motion analysis with neuromorphic vision sensor [J]. IEEE Sensors Journal, 2020, 20(11): 6170‒6181.
[72] Chen G, Wang F, Li W J, et al. NeuroIV: Neuromorphic vision meets intelligent vehicle towards safe driving with a new database and baseline evaluations [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(2): 1171‒1183.
[73] Ryan C, O´Sullivan B, Elrasad A, et al. Real-time face & eye tracking and blink detection using event cameras [J]. Neural Networks, 2021, 141: 87‒97.
[74] Ryan C, Elrasad A, Shariff W, et al. Real-time multi-task facial analytics with event cameras [J]. IEEE Access, 2023, 11: 76964‒76976.
[75] Yang C, Liu P G, Chen G, et al. Event-based driver distraction detection and action recognition [C]. Bedford: 2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2022.
[76] Liu P G, Chen G, Li Z J, et al. NeuroDFD: Towards efficient driver face detection with neuromorphic vision sensor [C]. Guilin: 2022 International Conference on Advanced Robotics and Mechatronics (ICARM), 2022.
[77] Becattini F, Palai F, Del Bimbo A. Understanding human reactions looking at facial microexpressions with an event camera [J]. IEEE Transactions on Industrial Informatics, 2022, 18(12): 9112‒9121.
[78] Berlincioni L, Cultrera L, Albisani C, et al. Neuromorphic event-based facial expression recognition [C]. Vancouver: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023.
[79] Zhang H W, Zhang J Q, Dong B, et al. In the blink of an eye: Event-based emotion recognition [C]. New York: ACM SIGGRAPH 2023 Conference Proceedings, 2023.
[80] Cook M, Gugelmann L, Jug F, et al. Interacting maps for fast visual interpretation [C]. San Jose: The 2011 International Joint Conference on Neural Networks, 2011.
[81] Kim H, Handa A, Benosman R, et al. Simultaneous mosaicing and tracking with an event camera [C]. Nottingham: Proceedings of the British Machine Vision Conference 2014, 2014.
[82] Kim H, Leutenegger S, Davison A J. Real-time 3D reconstruction and 6-DoF tracking with an event camera [C]. Amsterdam: Computer Vision–ECCV 2016, 2016.
[83] Bardow P, Davison A J, Leutenegger S. Simultaneous optical flow and intensity estimation from an event camera [C]. Las Vegas: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
[84] Munda G, Reinbacher C, Pock T. Real-time intensity-image reconstruction for event cameras using manifold regularisation [J]. International Journal of Computer Vision, 2018, 126(12): 1381‒1393.
[85] Barua S, Miyatani Y, Veeraraghavan A. Direct face detection and video reconstruction from event cameras [C]. Lake Placid: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016.
[86] Zhu L, Dong S W, Li J N, et al. Retina-like visual image reconstruction via spiking neural model [C]. Seattle: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
[87] Duwek H C, Shalumov A, Tsur E E. Image reconstruction from neuromorphic event cameras using Laplacian-prediction and poisson integration with spiking and artificial neural networks [C]. Nashville: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021.
[88] Scheerlinck C, Rebecq H, Gehrig D, et al. Fast image reconstruction with an event camera [C]. Snowmass: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 2020.
[89] Stoffregen T, Scheerlinck C, Scaramuzza D, et al. Reducing the sim-to-real gap for event cameras [C]. Glasgow: Computer Vision–ECCV 2020, 2020.
[90] Wang L, Kim T K, Yoon K J. EventSR: from asynchronous events to image reconstruction, restoration, and super-resolution via end-to-end adversarial learning [C]. Seattle: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
[91] Wang L, Kim T K, Yoon K J. Joint framework for single image reconstruction and super-resolution with an event camera [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(11): 7657‒7673.
[92] Mostafavi I S M, Choi J, Yoon K J. Learning to super resolve intensity images from events [C]. Seattle: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020: 2768‒2776.
[93] Wang B S, He J W, Yu L, et al. Event enhanced high-quality image recovery [C]. Glasgow: Computer Vision–ECCV 2020, 2020.
[94] Han J, Yang Y X, Zhou C, et al. EvIntSR-net: Event guided multiple latent frames reconstruction and super-resolution [C]. Montreal: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021.
[95] Duan P Q, Wang Z W, Zhou X Y, et al. EventZoom: Learning to denoise and super resolve neuromorphic events [C]. Nashville: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
[96] Rebecq H, Ranftl R, Koltun V, et al. Events-to-video: Bringing modern computer vision to event cameras [C]. Long Beach: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
[97] Rebecq H, Ranftl R, Koltun V, et al. High speed and high dynamic range video with an event camera [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(6): 1964‒1980.
[98] Wang L, Mohammad Mostafavi I S, Ho Y S, et al. Event-based high dynamic range image and very high frame rate video generation using conditional generative adversarial networks [C]. Long Beach: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
[99] Zhu L, Wang X, Chang Y, et al. Event-based video reconstruction via potential-assisted spiking neural network [C]. New Orleans: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[100] Pan L Y, Scheerlinck C, Yu X, et al. Bringing a blurry frame alive at high frame-rate with an event camera [C]. Long Beach: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
[101] Pan L Y, Hartley R, Scheerlinck C, et al. High frame rate video reconstruction based on an event camera [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(5): 2519‒2533.
[102] Weng W M, Zhang Y Y, Xiong Z W. Event-based video reconstruction using transformer [C]. Montreal: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021.
[103] Stoffregen T, Scheerlinck C, Scaramuzza D, et al. Reducing the sim-to-real gap for event cameras [C]. Glasgow: Computer Vision–ECCV 2020: 16th European Conference, 2020.
[104] Mueggler E, Rebecq H, Gallego G, et al. The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM [J]. The International Journal of Robotics Research, 2017, 36(2): 142‒149.
[105] Zhu A Z, Thakur D, Özaslan T, et al. The multivehicle stereo event camera dataset: An event camera dataset for 3D perception [J]. IEEE Robotics and Automation Letters, 2018, 3(3): 2032‒2039.
[106] Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: From error visibility to structural similarity [J]. IEEE Transactions on Image Processing, 2004, 13(4): 600‒612.
[107] Zhang R, Isola P, Efros A A, et al. The unreasonable effectiveness of deep features as a perceptual metric [C]. Salt Lake City: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.
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