MGS-SLAM: Monocular Sparse Tracking and Gaussian Mapping with Depth Smooth Regularization

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A. J. Davison, I. D. Reid, N. D. Molton, and O. Stasse, “Monoslam: Real-time single camera slam,” IEEE transactions on pattern analysis and machine intelligence , vol. 29, no. 6, pp. 1052–1067, 2007

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R. A. Newcombe, S. J. Lovegrove, and A. J. Davison, “Dtam: Dense tracking and mapping in real-time,” in 2011 international conference on computer vision . IEEE, 2011, pp. 2320–2327

2011

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J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, “A benchmark for the evaluation of rgb-d slam systems,” in 2012 IEEE/RSJ international conference on intelligent robots and systems . IEEE, 2012, pp. 573–580

2012

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J. Engel, T. Schöps, and D. Cremers, “Lsd-slam: Large-scale direct monocular slam,” in European conference on computer vision . Springer, 2014, pp. 834–849

2014

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A. Handa, T. Whelan, J. McDonald, and A. Davison, “A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM,” in IEEE Intl. Conf. on Robotics and Automation, ICRA , Hong Kong, China, May 2014

2014

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R. Mur-Artal, J. M. M. Montiel, and J. D. Tardos, “Orb-slam: a versatile and accurate monocular slam system,” IEEE transactions on robotics , vol. 31, no. 5, pp. 1147–1163, 2015

2015

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A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 5828–5839

2017

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J. Straub, T. Whelan, L. Ma, Y. Chen, E. Wijmans, S. Green, J. J. Engel, R. Mur-Artal, C. Ren, S. Verma, et al. , “The replica dataset: A digital replica of indoor spaces,” arXiv preprint arXiv:1906.05797 , 2019

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2019

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Z. Teed and J. Deng, “Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras,” Advances in neural information processing systems , vol. 34, pp. 16 558–16 569, 2021

2021

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H. Matsuki, R. Scona, J. Czarnowski, and A. J. Davison, “Codemapping: Real-time dense mapping for sparse slam using compact scene representations,” IEEE Robotics and Automation Letters , vol. 6, no. 4, pp. 7105–7112, 2021

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E. Sucar, S. Liu, J. Ortiz, and A. J. Davison, “imap: Implicit mapping and positioning in real-time,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 6229–6238

2021

Cited alongside, same era.

Z. Zhu, S. Peng, V. Larsson, W. Xu, H. Bao, Z. Cui, M. R. Oswald, and M. Pollefeys, “Nice-slam: Neural implicit scalable encoding for slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 12 786–12 796

2022

Cited alongside, same era.

M. M. Johari, C. Carta, and F. Fleuret, “Eslam: Efficient dense slam system based on hybrid representation of signed distance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 408–17 419

2023

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H. Wang, J. Wang, and L. Agapito, “Co-slam: Joint coordinate and sparse parametric encodings for neural real-time slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 293–13 302

2023

Later among the works it cites.

L. Liso, E. Sandström, V. Yugay, L. Van Gool, and M. R. Oswald, “Loopy-slam: Dense neural slam with loop closures,” arXiv preprint arXiv:2402.09944 , 2024

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2024

Closest in time.

T. Deng, G. Shen, T. Qin, J. Wang, W. Zhao, J. Wang, D. Wang, and W. Chen, “Plgslam: Progressive neural scene represenation with local to global bundle adjustment,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 19 657–19 666