Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas. 2018. Learning representations and generative models for 3D point clouds. In Proceedings of the International Conference on Machine Learning. 40-49.
Khalid M. Al-Gethami, Mousa T. Al-Akhras, and Mohammed Alawairdhi. 2021. Empirical evaluation of noise influence on supervised machine learning algorithms using intrusion detection datasets. Security and Communication Networks 2021 (2021), 1-28.
Sercan Ö Arik and Tomas Pfister. 2021. TabNet: Attentive interpretable tabular learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 6679-6687.
Iro Armeni, Ozan Sener, Amir R. Zamir, Helen Jiang, Ioannis Brilakis, Martin Fischer, and Silvio Savarese. 2016. 3D semantic parsing of large-scale indoor spaces. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1534-1543.
Sanjib Basu and Anirban DasGupta. 1997. The mean, median, and mode of unimodal distributions: A characterization. Theory of Probability & Its Applications 41, 2 (1997), 210-223.
Saifullahi Aminu Bello, Shangshu Yu, Cheng Wang, Jibril Muhmmad Adam, and Jonathan Li. 2020. Deep learning on 3D point clouds. Remote Sensing 12, 11 (2020), 1729.
Matteo Biagiola and Paolo Tonella. 2022. Testing the plasticity of reinforcement learning-based systems. ACM Transactions on Software Engineering and Methodology 31, 4 (2022), 1-46.
Dorit Borrmann, Andreas Nuechter, and Thomas Wiemann. 2018. Large-scale 3D point cloud processing for mixed and augmented reality. In Proceedings of the 2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct '18). IEEE.
Leo Breiman. 2001. Random forests. Machine Learning 45, 1 (2001), 5-32.
Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu. 2015. ShapeNet: An information-rich 3D model repository. arXiv preprint arXiv: 1512. 03012 (2015).
Junjie Chen. 2018. Learning to accelerate compiler testing. In Proceedings of the 40th International Conference on Software Engineering: Companion Proceedings. 472-475.
Junjie Chen, Yanwei Bai, Dan Hao, Yingfei Xiong, Hongyu Zhang, and Bing Xie. 2017. Learning to prioritize test programs for compiler testing. In Proceedings of the 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE '17). IEEE, 700-711.
Junjie Chen, Guancheng Wang, Dan Hao, Yingfei Xiong, Hongyu Zhang, Lu Zhang, and Bing Xie. 2018. Coverage prediction for accelerating compiler testing. IEEE Transactions on Software Engineering 47, 2 (2018), 261-278.
Junjie Chen, Zhuo Wu, Zan Wang, Hanmo You, Lingming Zhang, and Ming Yan. 2020. Practical accuracy estimation for efficient deep neural network testing. ACM Transactions on Software Engineering and Methodology 29, 4 (2020), 1-35.
Qi Chen, Sihai Tang, Qing Yang, and Song Fu. 2019. Cooper: Cooperative perception for connected autonomous vehicles based on 3D point clouds. In Proceedings of the 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS '19). IEEE, 514-524.
Siheng Chen, Baoan Liu, Chen Feng, Carlos Vallespi-Gonzalez, and Carl Wellington. 2020. 3D point cloud processing and learning for autonomous driving: Impacting map creation, localization, and perception. IEEE Signal Processing Magazine 38, 1 (2020), 68-86.
Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 785-794.
Chuang-Yuan Chiu, Michael Thelwell, Terry Senior, Simon Choppin, John Hart, and JonWheat. 2019. Comparison of depth cameras for three-dimensional reconstruction in medicine. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 233, 9 (2019), 938-947.
Yaodong Cui, Ren Chen, Wenbo Chu, Long Chen, Daxin Tian, Ying Li, and Dongpu Cao. 2021. Deep learning for image and point cloud fusion in autonomous driving: A review. IEEE Transactions on Intelligent Transportation Systems 23, 2 (2021), 722-739.
Xueqi Dang, Yinghua Li, Mike Papadakis, Jacques Klein, Tegawendé F. Bissyandé, and Yves Le Traon. 2024. Test input prioritization for machine learning classifiers. IEEE Transactions on Software Engineering. Preprint.
Xueqi Dang, Yinghua Li, Mike Papadakis, Jacques Klein, Tegawendé F. Bissyandé, and Yves L. E. Traon. 2023. Graph-Prior: Mutation-based test input prioritization for graph neural networks. ACM Transactions on Software Engineering and Methodology 33, 1 (2023), Article 22, 40 pages.
Daniel Di Nardo, Nadia Alshahwan, Lionel Briand, and Yvan Labiche. 2013. Coverage-based test case prioritisation: An industrial case study. In Proceedings of the 2013 IEEE 6th International Conference on Software Testing, Verification, and Validation. IEEE, 302-311.
Swaroopa Dola, Matthew B. Dwyer, and Mary Lou Soffa. 2023. Input distribution coverage: Measuring feature interaction adequacy in neural network testing. ACM Transactions on Software Engineering and Methodology 32, 3 (2023), 1-48.
Bertrand Douillard, James Underwood, Noah Kuntz, Vsevolod Vlaskine, Alastair Quadros, Peter Morton, and Alon Frenkel. 2011. On the segmentation of 3D LIDAR point clouds. In Proceedings of the 2011 IEEE International Conference on Robotics and Automation. IEEE, 2798-2805.
Len Du. 2020. How much deep learning does neural style transfer really need? An ablation study. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 3150-3159.
Sebastian Elbaum, Alexey G. Malishevsky, and Gregg Rothermel. 2002. Test case prioritization: A family of empirical studies. IEEE Transactions on Software Engineering 28, 2 (2002), 159-182.
Hazem Fahmy, Fabrizio Pastore, Lionel Briand, and Thomas Stifter. 2023. Simulator-based explanation and debugging of hazard-triggering events in DNN-based safety-critical systems. ACM Transactions on Software Engineering and Methodology 32, 4 (2023), 1-47.
Yang Feng, Qingkai Shi, Xinyu Gao, Jun Wan, Chunrong Fang, and Zhenyu Chen. 2020. DeepGini: Prioritizing massive tests to enhance the robustness of deep neural networks. In Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis. 177-188.
Simos Gerasimou, Hasan Ferit Eniser, Alper Sen, and Alper Cakan. 2020. Importance-driven deep learning system testing. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering. 702-713.
Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, and Mohammed Bennamoun. 2020. Deep learning for 3D point clouds: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 12 (2020), 4338-4364.
Christopher Henard, Mike Papadakis, Mark Harman, Yue Jia, and Yves Le Traon. 2016. Comparing white-box and black-box test prioritization. In Proceedings of the 2016 IEEE/ACM38th International Conference on Software Engineering (ICSE '16). IEEE, 523-534.
Qiang Hu, Yuejun Guo, Maxime Cordy, Xiaofei Xie, Lei Ma, Mike Papadakis, and Yves Le Traon. 2022. An empirical study on data distribution-aware test selection for deep learning enhancement. ACM Transactions on Software Engineering and Methodology 31, 4 (2022), 1-30.
Qiang Hu, Yuejun Guo, Maxime Cordy, Xiaofei Xie, Wei Ma, Mike Papadakis, and Yves Le Traon. 2021. Towards exploring the limitations of active learning: An empirical study. In Proceedings of the 2021 36th IEEE/ACMInternational Conference on Automated Software Engineering (ASE '21). IEEE, 917-929.
Qiang Hu, Lei Ma, Xiaofei Xie, Bing Yu, Yang Liu, and Jianjun Zhao. 2019. DeepMutation++: A mutation testing framework for deep learning systems. In Proceedings of the 2019 34th IEEE/ACMInternational Conference on Automated Software Engineering (ASE '19). IEEE, 1158-1161.
Tianxin Huang and Yong Liu. 2019. 3D point cloud geometry compression on deep learning. In Proceedings of the 27th ACM International Conference on Multimedia. 890-898.
Xinyu Huang, Xinjing Cheng, Qichuan Geng, Binbin Cao, Dingfu Zhou, PengWang, Yuanqing Lin, and Ruigang Yang. 2018. The ApolloScape dataset for autonomous driving. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 954-960.
Nargiz Humbatova, Gunel Jahangirova, and Paolo Tonella. 2021. DeepCrime: Mutation testing of deep learning systems based on real faults. In Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis. 67-78.
Anastasia Ioannidou, Elisavet Chatzilari, Spiros Nikolopoulos, and Ioannis Kompatsiaris. 2017. Deep learning advances in computer vision with 3D data: A survey. ACM Computing Surveys 50, 2 (2017), 1-38.
Gunel Jahangirova and Paolo Tonella. 2020. An empirical evaluation of mutation operators for deep learning systems. In Proceedings of the 2020 IEEE 13th International Conference on Software Testing, Validation, and Verification (ICST '20). IEEE, 74-84.
Yue Jia and Mark Harman. 2010. An analysis and survey of the development of mutation testing. IEEE Transactions on Software Engineering 37, 5 (2010), 649-678.
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Light-GBM: A highly efficient gradient boosting decision tree. In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS '17). 1-9.
Ken Kelley and Kristopher J. Preacher. 2012. On effect size. Psychological Methods 17, 2 (2012), 137.
Alex Kendall and Yarin Gal. 2017. What uncertainties do we need in Bayesian deep learning for computer vision? In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS '17). 1-11.
Been Kim, Rajiv Khanna, and Oluwasanmi O. Koyejo. 2016. Examples are not enough, learn to criticize! Criticism for interpretability. In Proceedings of the 30th Conference on Neural Information Processing Systems (NIPS '16). 1-9.
Jinhan Kim, Robert Feldt, and Shin Yoo. 2019. Guiding deep learning system testing using surprise adequacy. In Proceedings of the 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE '19). IEEE, 1039-1049.
Tae Kyun Kim. 2015. T test as a parametric statistic. Korean Journal of Anesthesiology 68, 6 (2015), 540-546.
Stephen Kokoska and Daniel Zwillinger. 2000. CRC Standard Probability and Statistics Tables and Formulae. CRC Press.
Martin G. Larson. 2008. Analysis of variance. Circulation 117, 1 (2008), 115-121.
Wim Lemkens, Prabhjot Kaur, Koen Buys, Peter Slaets, Tinne Tuytelaars, and Joris De Schutter. 2013. Multi RGB-D camera setup for generating large 3D point clouds. In Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 1092-1099.
Yinghua Li, Xueqi Dang, Haoye Tian, Tiezhu Sun, Zhijie Wang, Lei Ma, Jacques Klein, and Tegawende F. Bissyande. 2022. AI-driven mobile apps: An explorative study. arXiv preprint arXiv: 2212. 01635 (2022).
Zenan Li, Xiaoxing Ma, Chang Xu, Chun Cao, Jingwei Xu, and Jian Lü. 2019. Boosting operational DNN testing efficiency through conditioning. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 499-509.
Xingyu Liu, Mengyuan Yan, and Jeannette Bohg. 2019. MeteorNet: Deep learning on dynamic 3D point cloud sequences. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 9246-9255.
Yiling Lou, Junjie Chen, Lingming Zhang, and Dan Hao. 2019. A survey on regression test-case prioritization. In Advances in Computers. Vol. 113. Elsevier, 1-46.
Yiling Lou, Dan Hao, and Lu Zhang. 2015. Mutation-based test-case prioritization in software evolution. In Proceedings of the 2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE '15). IEEE, 46-57.
Lei Ma, Felix Juefei-Xu, Minhui Xue, Bo Li, Li Li, Yang Liu, and Jianjun Zhao. 2019. DeepCT: Tomographic combinatorial testing for deep learning systems. In Proceedings of the 2019 IEEE 26th International Conference on Software Analysis, Evolution, and Reengineering (SANER '19). IEEE, 614-618.
Lei Ma, Felix Juefei-Xu, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Chunyang Chen, Ting Su, Li Li, Yang Liu, Jianjun Zhao, and Yadong Wang. 2018. DeepGauge: Multi-granularity testing criteria for deep learning systems. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering. 120-131.
Lei Ma, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Felix Juefei-Xu, Chao Xie, Li Li, Yang Liu, Jianjun Zhao, and Yadong Wang. 2018. DeepMutation: Mutation testing of deep learning systems. In Proceedings of the 2018 IEEE 29th International Symposium on Software Reliability Engineering (ISSRE '18). IEEE, 100-111.
Wei Ma, Mike Papadakis, Anestis Tsakmalis, Maxime Cordy, and Yves Le Traon. 2021. Test selection for deep learning systems. ACM Transactions on Software Engineering and Methodology 30, 2 (2021), 1-22.
Bilawal Mahmood and SangUk Han. 2019. 3D registration of indoor point clouds for augmented reality. In Proceedings of the 2019 ASCE International Conference on Computing in Civil Engineering. 1-8.
Bilawal Mahmood, SangUk Han, and Dong-Eun Lee. 2020. BIM-based registration and localization of 3D point clouds of indoor scenes using geometric features for augmented reality. Remote Sensing 12, 14 (2020), 2302.
Thomas P. Minka. 2003. A comparison of numerical optimizers for logistic regression. Unpublished Draft.
Quang Hung Nguyen, Hai-Bang Ly, Lanh Si Ho, Nadhir Al-Ansari, Hiep Van Le, Van Quan Tran, Indra Prakash, and Binh Thai Pham. 2021. Influence of data splitting on performance of machine learning models in prediction of shear strength of soil. Mathematical Problems in Engineering 2021 (2021), 1-15.
Sebastián Ortega, José Miguel Santana, JochenWendel, Agustín Trujillo, and Syed Monjur Murshed. 2021. Generating 3D city models from open LiDAR point clouds: Advancing towards smart city applications. In Open Source Geospatial Science for Urban Studies. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, 97-116.
Annibale Panichella, Fitsum Meshesha Kifetew, and Paolo Tonella. 2017. Automated test case generation as a manyobjective optimisation problem with dynamic selection of the targets. IEEE Transactions on Software Engineering 44, 2 (2017), 122-158.
Annibale Panichella, Fitsum Meshesha Kifetew, and Paolo Tonella. 2018. A large scale empirical comparison of stateof-the-art search-based test case generators. Information and Software Technology 104 (2018), 236-256.
Mike Papadakis, Christopher Henard, and Yves Le Traon. 2014. Sampling program inputs with mutation analysis: Going beyond combinatorial interaction testing. In Proceedings of the 7th IEEE International Conference on Software Testing, Verification, and Validation (ICST '14). IEEE, 1-10. https://doi. org/10. 1109/ICST. 2014. 11
Mike Papadakis, Marinos Kintis, Jie Zhang, Yue Jia, Yves Le Traon, and Mark Harman. 2019. Mutation testing advances: An analysis and survey. In Advances in Computers. Vol. 112. Elsevier, 275-378.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An imperative style, high-performance deep learning library. In Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS '19). 1-12.
Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana. 2017. DeepXplore: Automated whitebox testing of deep learning systems. In Proceedings of the 26th Symposium on Operating Systems Principles. 1-18.
François Pomerleau, Francis Colas, and Roland Siegwart 2015. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1 (2015), 1-104.
Michael Prince. 2004. Does active learning work? A review of the research. Journal of Engineering Education 93, 3 (2004), 223-231.
Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas. 2017. PointNet: Deep learning on point sets for 3D classification and segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 652-660.
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J. Guibas. 2017. PointNet++: Deep hierarchical feature learning on point sets in a metric space. In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS '17). 1-10.
Yan Qian, Peng Cao, Wenqing Yin, Fang Dai, Fei Hu, and Zhijun Yan. 2017. Calculation method of surface shape feature of rice seed based on point cloud. Computers and Electronics in Agriculture 142 (2017), 416-423.
Vincenzo Riccio, Nargiz Humbatova, Gunel Jahangirova, and Paolo Tonella. 2021. DeepMetis: Augmenting a deep learning test set to increase its mutation score. In Proceedings of the 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE '21). IEEE, 355-367.
Vincenzo Riccio and Paolo Tonella. 2020. Model-based exploration of the frontier of behaviours for deep learning system testing. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 876-888.
Jie Shao, Wuming Zhang, Nicolas Mellado, Pierre Grussenmeyer, Renju Li, Yiming Chen, Peng Wan, Xintong Zhang, and Shangshu Cai. 2019. Automated markerless registration of point clouds from TLS and structured light scanner for heritage documentation. Journal of Cultural Heritage 35 (2019), 16-24.
Weijun Shen, Jun Wan, and Zhenyu Chen. 2018. MuNN: Mutation analysis of neural networks. In Proceedings of the 2018 IEEE International Conference on Software Quality, Reliability, and Security Companion (QRS-C '18). IEEE, 108-115.
Donghwan Shin, Shin Yoo, Mike Papadakis, and Doo-Hwan Bae. 2019. Empirical evaluation of mutation-based test case prioritization techniques. Software Testing, Verification and Reliability 29, 1-2 (2019), e1695.
Martin Simony, Stefan Milzy, Karl Amendey, and Horst-Michael Gross. 2018. Complex-YOLO: An Euler-regionproposal for real-time 3D object detection on point clouds. In Proceedings of the European Conference on Computer Vision Workshops (ECCV '18). 1-14.
Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, and Joel S. Emer. 2017. Efficient processing of deep neural networks: A tutorial and survey. Proceedings of the IEEE 105, 12 (2017), 2295-2329.
Paolo Tonella, Paolo Avesani, and Angelo Susi. 2006. Using the case-based ranking methodology for test case prioritization. In Proceedings of the 2006 22nd IEEE International Conference on Software Maintenance. IEEE, 123-133.
Mikaela Angelina Uy, Quang-Hieu Pham, Binh-Son Hua, Thanh Nguyen, and Sai-Kit Yeung. 2019. Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 1588-1597.
DanWang and Yi Shang. 2014. A new active labeling method for deep learning. In Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN '14). IEEE, 112-119.
Qian Wang and Min-Koo Kim. 2019. Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018. Advanced Engineering Informatics 39 (2019), 306-319.
Yue Wang and Justin M. Solomon. 2019. Deep closest point: Learning representations for point cloud registration. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 3523-3532.
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, and Justin M. Solomon. 2019. Dynamic graph CNN for learning on point clouds. ACM Transactions on Graphics 38, 5 (2019), 1-12.
Zan Wang, Hanmo You, Junjie Chen, Yingyi Zhang, Xuyuan Dong, and Wenbin Zhang. 2021. Prioritizing test inputs for deep neural networks via mutation analysis. In Proceedings of the 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE '21). IEEE, 397-409.
MichaelWeiss and Paolo Tonella. 2022. Simple techniqueswork surprisinglywell for neural network test prioritization and active learning (replicability study). In Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis. 139-150.
W. Eric Wong, Joseph R. Horgan, Saul London, and Aditya P. Mathur. 1995. Effect of test set minimization on fault detection effectiveness. In Proceedings of the 17th International Conference on Software Engineering. 41-50.
Wenxuan Wu, Zhongang Qi, and Li Fuxin. 2019. PointConv: Deep convolutional networks on 3D point clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9621-9630.
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao. 2015. 3D ShapeNets: A deep representation for volumetric shapes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1912-1920.
Shin Yoo and Mark Harman. 2012. Regression testing minimization, selection and prioritization: A survey. Software Testing, Verification and Reliability 22, 2 (2012), 67-120.
Shin Yoo, Mark Harman, Paolo Tonella, and Angelo Susi. 2009. Clustering test cases to achieve effective and scalable prioritisation incorporating expert knowledge. In Proceedings of the 18th International Symposium on Software Testing and Analysis. 201-212.
Xiangyu Yue, Bichen Wu, Sanjit A. Seshia, Kurt Keutzer, and Alberto L. Sangiovanni-Vincentelli. 2018. A LiDAR point cloud generator: From a virtual world to autonomous driving. In Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval. 458-464.
Jiaying Zhang, Xiaoli Zhao, Zheng Chen, and Zhejun Lu. 2019. A review of deep learning-based semantic segmentation for point cloud. IEEE Access 7 (2019), 179118-179133.
Zhiyuan Zhang, Yuchao Dai, and Jiadai Sun. 2020. Deep learning based point cloud registration: An overview. Virtual Reality & Intelligent Hardware 2, 3 (2020), 222-246.
