ChangeSim: Towards End-to-End Online Scene Change Detection in Industrial Indoor Environments

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2014

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K. Sakurada and T. Okatani, “Change detection from a street image pair using cnn features and superpixel segmentation.” in Proc. British Machine Vision Conference (BMVC) , vol. 61, 2015, pp. 1–12

2015

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A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. Van Der Smagt, D. Cremers, and T. Brox, “Flownet: Learning optical flow with convolutional networks,” in Proc. IEEE International Conference on Computer Vision (ICCV) , 2015, pp. 2758–2766

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2015

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R. Ambrus, J. Folkesson, and P. Jensfelt, “Unsupervised object segmentation through change detection in a long term autonomy scenario,” in Proc. IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids) , 2016, pp. 1181–1187

2016

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J. Mueller and A. Thyagarajan, “Siamese recurrent architectures for learning sentence similarity,” in Proc. AAAI Conference on Artificial Intelligence , vol. 30, no. 1, 2016

2016

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M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 3213–3223

2016

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M. Fehr, F. Furrer, I. Dryanovski, J. Sturm, I. Gilitschenski, R. Siegwart, and C. Cadena, “Tsdf-based change detection for consistent long-term dense reconstruction and dynamic object discovery,” in Proc. IEEE International Conference on Robotics and automation (ICRA) , 2017, pp. 5237–5244

2017

Cited alongside, same era.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 40, no. 4, pp. 834–848, 2017

2017

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P. F. Alcantarilla, S. Stent, G. Ros, R. Arroyo, and R. Gherardi, “Street-view change detection with deconvolutional networks,” Autonomous Robots , vol. 42, no. 7, pp. 1301–1322, 2018

2018

Cited alongside, same era.

W. Li, S. Saeedi, J. McCormac, R. Clark, D. Tzoumanikas, Q. Ye, Y. Huang, R. Tang, and S. Leutenegger, “Interiornet: Mega-scale multi-sensor photo-realistic indoor scenes dataset,” in Proc. British Machine Vision Conference (BMVC) , 2018

L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proc. European Conference on Computer Vision (ECCV) , 2018, pp. 801–818

2018

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J. Wald, A. Avetisyan, N. Navab, F. Tombari, and M. Nießner, “Rio: 3d object instance re-localization in changing indoor environments,” in Proc. IEEE/CVF International Conference on Computer Vision (ICCV) , 2019, pp. 7658–7667

2019

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M. Labbé and F. Michaud, “Rtab-map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation,” Journal of Field Robotics , vol. 36, no. 2, pp. 416–446, 2019

2019

Later among the works it cites.

E. Langer, T. Patten, and M. Vincze, “Robust and efficient object change detection by combining global semantic information and local geometric verification,” in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020