Fetching the paper…
Reading the bibliography…
We propose SemGauss-SLAM, a dense semantic SLAM system utilizing 3D Gaussian representation, that enables accurate 3D semantic mapping, robust camera tracking, and high-quality rendering simultaneously.
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
Earlier work this paper cites.
J. McCormac, A. Handa, A. Davison, and S. Leutenegger, “Semanticfusion: Dense 3d semantic mapping with convolutional neural networks,” in 2017 IEEE International Conference on Robotics and automation (ICRA) . IEEE, 2017, pp. 4628–4635
2017
Earlier work this paper cites.
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
Earlier work this paper cites.
K.-N. Lianos, J. L. Schonberger, M. Pollefeys, and T. Sattler, “Vso: Visual semantic odometry,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 234–250
2018
Earlier work this paper cites.
J. McCormac, R. Clark, M. Bloesch, A. Davison, and S. Leutenegger, “Fusion++: Volumetric object-level slam,” in 2018 international conference on 3D vision (3DV) . IEEE, 2018, pp. 32–41
2018
Earlier work this paper cites.
M. Grinvald, F. Furrer, T. Novkovic, J. J. Chung, C. Cadena, R. Siegwart, and J. Nieto, “Volumetric instance-aware semantic mapping and 3d object discovery,” IEEE Robotics and Automation Letters , vol. 4, no. 3, pp. 3037–3044, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Rosinol, M. Abate, Y. Chang, and L. Carlone, “Kimera: an open-source library for real-time metric-semantic localization and mapping,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 1689–1696
2020
Earlier work this paper cites.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021
2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Y. Bao, Z. Yang, Y. Pan, and R. Huan, “Semantic-direct visual odometry,” IEEE Robotics and Automation Letters , vol. 7, no. 3, pp. 6718–6725, 2022
2022
Earlier work this paper cites.
Y. Tian, Y. Chang, F. H. Arias, C. Nieto-Granda, J. P. How, and L. Carlone, “Kimera-multi: Robust, distributed, dense metric-semantic slam for multi-robot systems,” IEEE Transactions on Robotics , vol. 38, no. 4, 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
L. Schmid, J. Delmerico, J. L. Schönberger, J. Nieto, M. Pollefeys, R. Siegwart, and C. Cadena, “Panoptic multi-tsdfs: a flexible representation for online multi-resolution volumetric mapping and long-term dynamic scene consistency,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 8018–8024
2022
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
Cited alongside, same era.
K. Li, M. Niemeyer, N. Navab, and F. Tombari, “Dns-slam: Dense neural semantic-informed slam,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2024, pp. 7839–7846
2024
Closest in time.
C. Yan, D. Qu, D. Xu, B. Zhao, Z. Wang, D. Wang, and X. Li, “Gs-slam: Dense visual slam with 3d gaussian splatting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 19 595–19 604
2024
Closest in time.
N. Keetha, J. Karhade, K. M. Jatavallabhula, G. Yang, S. Scherer, D. Ramanan, and J. Luiten, “Splatam: Splat track & map 3d gaussians for dense rgb-d slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 21 357–21 366
2024
Closest in time.
H. Matsuki, R. Murai, P. H. Kelly, and A. J. Davison, “Gaussian splatting slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 18 039–18 048
2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2023
Cited alongside, same era.
2023
Cited alongside, same era.
B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering,” ACM Transactions on Graphics , vol. 42, no. 4, 2023
2023
Cited alongside, same era.
2023
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
Cited alongside, same era.
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
Cited alongside, same era.
E. Sandström, Y. Li, L. Van Gool, and M. R. Oswald, “Point-slam: Dense neural point cloud-based slam,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 18 433–18 444
2023
Cited alongside, same era.
X. Kong, S. Liu, M. Taher, and A. J. Davison, “vmap: Vectorised object mapping for neural field slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 952–961
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Closest in time.
H. Huang, L. Li, H. Cheng, and S.-K. Yeung, “Photo-slam: Real-time simultaneous localization and photorealistic mapping for monocular stereo and rgb-d cameras,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 21 584–21 593
2024
Closest in time.
M. Li, S. Liu, H. Zhou, G. Zhu, N. Cheng, T. Deng, and H. Wang, “Sgs-slam: Semantic gaussian splatting for neural dense slam,” in European Conference on Computer Vision . Springer, 2024, pp. 163–179
2024
Closest in time.
J. Hu, M. Mao, H. Bao, G. Zhang, and Z. Cui, “Cp-slam: Collaborative neural point-based slam system,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
Y. Yan, H. Lin, C. Zhou, W. Wang, H. Sun, K. Zhan, X. Lang, X. Zhou, and S. Peng, “Street gaussians: Modeling dynamic urban scenes with gaussian splatting,” in European Conference on Computer Vision . Springer, 2024, pp. 156–173
2024
Closest in time.
X. Zhou, Z. Lin, X. Shan, Y. Wang, D. Sun, and M.-H. Yang, “Drivinggaussian: Composite gaussian splatting for surrounding dynamic autonomous driving scenes,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2024, pp. 21 634–21 643
2024
Closest in time.
M. Ye, M. Danelljan, F. Yu, and L. Ke, “Gaussian grouping: Segment and edit anything in 3d scenes,” in European Conference on Computer Vision . Springer, 2024, pp. 162–179
2024
Closest in time.
S. Zhou, H. Chang, S. Jiang, Z. Fan, Z. Zhu, D. Xu, P. Chari, S. You, Z. Wang, and A. Kadambi, “Feature 3dgs: Supercharging 3d gaussian splatting to enable distilled feature fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 21 676–21 685
2024
Closest in time.
J. Zhuang, D. Kang, Y.-P. Cao, G. Li, L. Lin, and Y. Shan, “Tip-editor: An accurate 3d editor following both text-prompts and image-prompts,” ACM Transactions on Graphics (TOG) , vol. 43, no. 4, pp. 1–12, 2024
2024
Closest in time.