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In this letter, we present a neural field-based real-time monocular mapping framework for accurate and dense Simultaneous Localization and Mapping (SLAM).
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C. Campos, R. Elvira, J. J. G. Rodríguez, J. M. Montiel, and J. D. Tardós, “ORB-SLAM3: An accurate open-source library for visual, visual-inertial, and multimap slam,” IEEE Transactions on Robotics , vol. 37, no. 6, pp. 1874–1890, 2021
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
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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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A. Eftekhar, A. Sax, J. Malik, and A. Zamir, “Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 10 786–10 796
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2022
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2023
Closest in time.
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
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L. Yariv, J. Gu, Y. Kasten, and Y. Lipman, “Volume rendering of neural implicit surfaces,” Advances in Neural Information Processing Systems , vol. 34, pp. 4805–4815, 2021
2021
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Z. Teed and J. Deng, “Tangent space backpropagation for 3d transformation groups,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 10 338–10 347
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.
X. Yang, H. Li, H. Zhai, Y. Ming, Y. Liu, and G. Zhang, “Vox-fusion: Dense tracking and mapping with voxel-based neural implicit representation,” in 2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) . IEEE, 2022, pp. 499–507
2022
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L. Koestler, N. Yang, N. Zeller, and D. Cremers, “Tandem: Tracking and dense mapping in real-time using deep multi-view stereo,” in Conference on Robot Learning . PMLR, 2022, pp. 34–45
2022
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2022
Cited alongside, same era.
T. Müller, A. Evans, C. Schied, and A. Keller, “Instant neural graphics primitives with a multiresolution hash encoding,” ACM Transactions on Graphics (ToG) , vol. 41, no. 4, pp. 1–15, 2022
2022
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Z. Yu, S. Peng, M. Niemeyer, T. Sattler, and A. Geiger, “Monosdf: Exploring monocular geometric cues for neural implicit surface reconstruction,” Advances in neural information processing systems , vol. 35, pp. 25 018–25 032, 2022
2022
Cited alongside, same era.
Closest in time.
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
Closest in time.
Y. Zhang, F. Tosi, S. Mattoccia, and M. Poggi, “Go-slam: Global optimization for consistent 3d instant reconstruction,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2023
2023
Closest in time.
2023
Closest in time.
C.-M. Chung, Y.-C. Tseng, Y.-C. Hsu, X.-Q. Shi, Hua, and W. H. Hsu, “Orbeez-slam: A real-time monocular visual slam with orb features and nerf-realized mapping,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023
2023
Closest in time.
W. Zhang, S. Wang, X. Dong, R. Guo, and N. Haala, “Bamf-slam: Bundle adjusted multi-fisheye visual-inertial slam using recurrent field transforms,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023, pp. 6232–6238
2023
Closest in time.
A. Rosinol, J. J. Leonard, and L. Carlone, “Probabilistic volumetric fusion for dense monocular slam,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2023, pp. 3097–3105
2023
Closest in time.
H. Matsuki, E. Sucar, T. Laidow, K. Wada, R. Scona, and A. J. Davison, “imode: Real-time incremental monocular dense mapping using neural field,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 4171–4177
2023
Closest in time.
W. Dong, C. Choy, C. Loop, O. Litany, Y. Zhu, and A. Anandkumar, “Fast monocular scene reconstruction with global-sparse local-dense grids,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 4263–4272
2023
Closest in time.