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A Review of Common Datasets and Algorithms for 3D Gaussian Splatting SLAM in Dynamic Scenes

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DOI: 10.23977/acss.2026.100112 | Downloads: 26 | Views: 582

Author(s)

Enbo Zhang 1

Affiliation(s)

1 Yunnan Normal University, Kunming, Yunnan, China

Corresponding Author

Enbo Zhang

ABSTRACT

Simultaneous Localization and Mapping (SLAM) is a fundamental technology for autonomous navigation, augmented reality, and virtual reality. However, dynamic factors widely present in real-world environments, such as pedestrians, vehicles, and movable objects, severely violate the static-world assumption adopted by traditional SLAM methods, posing significant challenges to localization accuracy, map consistency, and long-term stability. In recent years, 3D Gaussian Splatting (3DGS) has attracted increasing attention due to its compact and efficient scene representation, favorable differentiability, and excellent real-time rendering performance. It provides a novel technical paradigm for SLAM in dynamic environments and has gradually become a research hotspot. This paper presents a systematic review of 3D Gaussian SLAM algorithms in dynamic scenes. First, commonly used benchmark datasets are analyzed, including the TUM and BONN datasets for indoor dynamic environments and the KITTI dataset for outdoor scenarios. Comparisons are conducted in terms of scene scale, dynamic object types, and evaluation protocols. Second, commonly used evaluation metrics for pose accuracy, rendering quality, and system efficiency are summarized. Third, representative dynamic-scene 3D Gaussian SLAM algorithms are reviewed, and their core ideas and technical characteristics for handling dynamic interference are systematically analyzed. Finally, existing challenges are discussed, and future research directions are outlined.

KEYWORDS

3D Gaussian Splatting, SLAM, Dynamic Scenes

CITE THIS PAPER

Enbo Zhang. A Review of Common Datasets and Algorithms for 3D Gaussian Splatting SLAM in Dynamic Scenes. Advances in Computer, Signals and Systems (2026). Vol. 10, No. 1, 93-101. DOI: http://dx.doi.org/10.23977/acss.2026.100112.

REFERENCES

[1] Kerbl B, Kopanas G, Leimkühler T, et al. 3D Gaussian splatting for real-time radiance field rendering[J]. ACM Trans. Graph., 2023, 42(4): 139:1-139:14.
[2] Sturm J, Engelhard N, Endres F, et al. A benchmark for the evaluation of RGB-D SLAM systems[C]//2012 IEEE/RSJ international conference on intelligent robots and systems. IEEE, 2012: 573-580.
[3] Palazzolo E, Behley J, Lottes P, et al. ReFusion: 3D reconstruction in dynamic environments for RGB-D cameras exploiting residuals[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 7855-7862.
[4] Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? the kitti vision benchmark suite[C]//2012 IEEE conference on computer vision and pattern recognition. IEEE, 2012: 3354-3361.
[5] Xu Y, Jiang H, Xiao Z, et al. Dg-slam: Robust dynamic gaussian splatting slam with hybrid pose optimization[J]. Advances in Neural Information Processing Systems, 2024, 37: 51577-51596.
[6] Teed Z, Deng J. Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras[J]. Advances in neural information processing systems, 2021, 34: 16558-16569.
[7] Kong M, Lee J, Lee S, et al. DGS-SLAM: Gaussian splatting SLAM in dynamic environment[J]. arXiv preprint arXiv:2411.10722, 2024.
[8] Huang C, Zhang L, Deng T, et al. MPDG-SLAM: Motion Probability-Based 3DGS-SLAM in Dynamic Environment[C]//2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2025: 14045-14052.
[9] Liu Y, Fan K, Lan B, et al. DyPho-SLAM: Real-time Photorealistic SLAM in Dynamic Environments[C]//2025 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2025: 1-6.
[10] Hu X, Zhang C, Zhao M, et al. DyGS-SLAM: Real-Time Accurate Localization and Gaussian Reconstruction for Dynamic Scenes[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2025: 9561-9571.
[11] Liu H, Wang L, Luo H, et al. SDD-SLAM: Semantic-Driven Dynamic SLAM with Gaussian Splatting[J]. IEEE Robotics and Automation Letters, 2025.
[12] Zhu F, Zhao Y, Chen Z, et al. DyGS-SLAM: Realistic Map Reconstruction in Dynamic Scenes Based on Double-Constrained Visual SLAM[J]. Remote Sensing, 2025, 17(4): 625.
[13] Li M, Chen W, Cheng N, et al. GARAD-SLAM: 3D GAussian splatting for Real-time Anti Dynamic SLAM[J]. arXiv preprint arXiv:2502.03228, 2025.
[14] Wen L, Li S, Zhang Y, et al. Gassidy: Gaussian splatting slam in dynamic environments[C]//2025 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2025: 8471-8477.
[15] Wu W, Su C, Zhu S, et al. ADD-SLAM: Adaptive Dynamic Dense SLAM with Gaussian Splatting[J]. arXiv preprint arXiv:2505.19420, 2025.
[16] Li M, Zhou Y, Zhou H, et al. Dy3DGS-SLAM: Monocular 3D Gaussian Splatting SLAM for Dynamic Environments[J]. arXiv preprint arXiv:2506.05965, 2025.
[17] Zhou H, Chen J, Li Z. Dynamic SLAM with 3D Gaussian Splatting Supporting Monocular Sensing[J]. IEEE Sensors Journal, 2025.
[18] Zheng J, Zhu Z, Bieri V, et al. Wildgs-slam: Monocular gaussian splatting slam in dynamic environments[C]//Proceedings of the Computer Vision and Pattern Recognition Conference. 2025: 11461-11471.
[19] Deng Z, Wang R. SGF-SLAM: Semantic Gaussian Filtering SLAM for Urban Road Environments[J]. Sensors, 2025, 25(12): 3602.
[20] Zhu W, Li X, Xu Q, et al. LVD-GS: Gaussian Splatting SLAM for Dynamic Scenes via Hierarchical Explicit-Implicit Representation Collaboration Rendering[J]. arXiv preprint arXiv:2510.22669, 2025.
[21] Matsuki H, Bae G, Davison A J. 4DTAM: Non-Rigid Tracking and Mapping via Dynamic Surface Gaussians[C]//Proceedings of the Computer Vision and Pattern Recognition Conference. 2025: 26921-26932.
[22] Zhu S, Huang Y, Wu W, et al. D2GSLAM: 4D Dynamic Gaussian Splatting SLAM[J]. arXiv preprint arXiv:2512.09411, 2025.
[23] Sun Z, Lo J, Hu J. Embracing Dynamics: Dynamics-aware 4D Gaussian Splatting SLAM[J]. arXiv preprint arXiv:2504.04844, 2025.
[24] Huang K, Yang W, Zhou P, et al. JPG-SLAM: Joint point-Gaussian splatting representation for dense dynamic SLAM[C]//2025 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2025: 5018-5024.
[25] Zhang Z, Kaufmann M, Xue L, et al. ODHSR: Online Dense 3D Reconstruction of Humans and Scenes from Monocular Videos[C]//Proceedings of the Computer Vision and Pattern Recognition Conference. 2025: 21824-21835.
[26] Li H, Meng X, Zuo X, et al. PG-SLAM: Photo-realistic and geometry-aware RGB-D SLAM in dynamic environments[J]. IEEE Transactions on Robotics, 2025.

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