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2024

3D Gaussian Splatting in Robotics: A Survey

Zhu, Siting, Wang, Guangming, Kong, Xin et al.

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Dense 3D representations of the environment have been a long-term goal in the robotics field.

While previous Neural Radiance Fields (NeRF) representation have been prevalent for its implicit, coordinate-based model, the recent emergence of 3D Gaussian Splatting (3DGS) has demonstrated remarkable potential in its explicit radiance field representation.
By leveraging 3D Gaussian primitives for explicit scene representation and enabling differentiable rendering, 3DGS has shown significant advantages over other radiance fields in real-time rendering and photo-realistic performance, which is beneficial for robotic applications.
In this survey, we provide a comprehensive understanding of 3DGS in the field of robotics.

3D Gaussian Splatting in Robotics: A Survey · Around

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2024

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L. Li, L. Zhang, Z. Wang, and Y. Shen, “Gs 3 lam: Gaussian semantic splatting slam,” in ACM Multimedia 2024

2024

Closest in time.

J. Yu, K. Hari, K. Srinivas, K. El-Refai, A. Rashid, C. M. Kim, J. Kerr, R. Cheng, M. Z. Irshad, A. Balakrishna, T. Kollar, and K. Goldberg, “Language-embedded gaussian splats (legs): Incrementally building room-scale representations with a mobile robot,” in Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS) , 2024

2024

Closest in time.

Y. Zheng, X. Chen, Y. Zheng, S. Gu, R. Yang, B. Jin, P. Li, C. Zhong, Z. Wang, L. Liu et al. , “Gaussiangrasper: 3d language gaussian splatting for open-vocabulary robotic grasping,” arXiv preprint arXiv:2403.09637 , 2024

2024

Closest in time.

O. Shorinwa, J. Tucker, A. Smith, A. Swann, T. Chen, R. Firoozi, M. Kennedy III, and M. Schwager, “Splat-mover: Multi-stage, open-vocabulary robotic manipulation via editable gaussian splatting,” arXiv preprint arXiv:2405.04378 , 2024

2024

Closest in time.

G. Lu, S. Zhang, Z. Wang, C. Liu, J. Lu, and Y. Tang, “Manigaussian: Dynamic gaussian splatting for multi-task robotic manipulation,” arXiv preprint arXiv:2403.08321 , 2024

2024

Closest in time.

Y. Li and D. Pathak, “Object-aware gaussian splatting for robotic manipulation,” in ICRA 2024 Workshop on 3D Visual Representations for Robot Manipulation

2024

Closest in time.

K. Jiang, Y. Fu, M. Varma T, Y. Belhe, X. Wang, H. Su, and R. Ramamoorthi, “A construct-optimize approach to sparse view synthesis without camera pose,” in ACM SIGGRAPH 2024 Conference Papers , 2024, pp. 1–11

2024

Closest in time.

P. Jiang, G. Pandey, and S. Saripalli, “3dgs-reloc: 3d gaussian splatting for map representation and visual relocalization,” arXiv preprint arXiv:2403.11367 , 2024

2024

Closest in time.

J.-M. Liu, H.-K. Yang, T.-C. Chiang, T.-R. Liu, C.-W. Huang, Q. Kong, N. Kobori, and C.-Y. Lee, “Enhancing visual relocalization with dense scene coordinates derived from 3d gaussian splatting,” in RoboNerF: 1st Workshop On Neural Fields In Robotics at ICRA 2024

2024

Closest in time.

M. Bortolon, T. Tsesmelis, S. James, F. Poiesi, and A. Del Bue, “6dgs: 6d pose estimation from a single image and a 3d gaussian splatting model,” arXiv preprint arXiv:2407.15484 , 2024

2024

Closest in time.

H. Li, Y. Gao, D. Zhang, C. Wu, Y. Dai, C. Zhao, H. Feng, E. Ding, J. Wang, and J. Han, “Ggrt: Towards generalizable 3d gaussians without pose priors in real-time,” arXiv preprint arXiv:2403.10147 , 2024

2024

Closest in time.

J. Chang, Y. Xu, Y. Li, Y. Chen, and X. Han, “Gaussreg: Fast 3d registration with gaussian splatting,” arXiv preprint arXiv:2407.05254 , 2024

2024

Closest in time.

C. Liu, S. Chen, Y. Bhalgat, S. Hu, Z. Wang, M. Cheng, V. A. Prisacariu, and T. Braud, “Gsloc: Efficient camera pose refinement via 3d gaussian splatting,” arXiv preprint arXiv:2408.11085 , 2024

Original

2024

Closest in time.

R. Jin, Y. Gao, H. Lu, and F. Gao, “Gs-planner: A gaussian-splatting-based planning framework for active high-fidelity reconstruction,” arXiv preprint arXiv:2405.10142 , 2024

2024

Closest in time.

X. Lei, M. Wang, W. Zhou, and H. Li, “Gaussnav: Gaussian splatting for visual navigation,” arXiv preprint arXiv:2403.11625 , 2024

2024

Closest in time.

T. Chen, O. Shorinwa, W. Zeng, J. Bruno, P. Dames, and M. Schwager, “Splat-nav: Safe real-time robot navigation in gaussian splatting maps,” arXiv preprint arXiv:2403.02751 , 2024

2024

Closest in time.

G. Liu, W. Jiang, B. Lei, V. Pandey, K. Daniilidis, and N. Motee, “Beyond uncertainty: Risk-aware active view acquisition for safe robot navigation and 3d scene understanding with fisherrf,” arXiv preprint arXiv:2403.11396 , 2024

2024

Closest in time.

J. Oh, J. Chung, D. Lee, and K. M. Lee, “Deblurgs: Gaussian splatting for camera motion blur,” arXiv preprint arXiv:2404.11358 , 2024

2024

Closest in time.

L. Zhao, P. Wang, and P. Liu, “Bad-gaussians: Bundle adjusted deblur gaussian splatting,” arXiv preprint arXiv:2403.11831 , 2024

2024

Closest in time.

W. Chen and L. Liu, “Deblur-gs: 3d gaussian splatting from camera motion blurred images,” Proc. of the ACM on Computer Graphics and Interactive Techniques , vol. 7, no. 1, pp. 1–15, 2024

2024

Closest in time.

F. Darmon, L. Porzi, S. Rota-Bulò, and P. Kontschieder, “Robust gaussian splatting,” arXiv preprint arXiv:2404.04211 , 2024

2024

Closest in time.

O. Seiskari, J. Ylilammi, V. Kaatrasalo, P. Rantalankila, M. Turkulainen, J. Kannala, E. Rahtu, and A. Solin, “Gaussian splatting on the move: Blur and rolling shutter compensation for natural camera motion,” arXiv preprint arXiv:2403.13327 , 2024

2024

Closest in time.

J. Lee, D. Kim, D. Lee, S. Cho, and S. Lee, “Crim-gs: Continuous rigid motion-aware gaussian splatting from motion blur images,” arXiv preprint arXiv:2407.03923 , 2024

2024

Closest in time.

C. Peng, Y. Tang, Y. Zhou, N. Wang, X. Liu, D. Li, and R. Chellappa, “Bags: Blur agnostic gaussian splatting through multi-scale kernel modeling,” arXiv preprint arXiv:2403.04926 , 2024

2024

Closest in time.

Y. Chen and G. H. Lee, “Dogaussian: Distributed-oriented gaussian splatting for large-scale 3d reconstruction via gaussian consensus,” arXiv preprint arXiv:2405.13943 , 2024

2024

Closest in time.

K. Ren, L. Jiang, T. Lu, M. Yu, L. Xu, Z. Ni, and B. Dai, “Octree-gs: Towards consistent real-time rendering with lod-structured 3d gaussians,” arXiv preprint arXiv:2403.17898 , 2024

2024

Closest in time.

Y. Liu, H. Guan, C. Luo, L. Fan, J. Peng, and Z. Zhang, “Citygaussian: Real-time high-quality large-scale scene rendering with gaussians,” arXiv preprint arXiv:2404.01133 , 2024

2024

Closest in time.

B. Kerbl, A. Meuleman, G. Kopanas, M. Wimmer, A. Lanvin, and G. Drettakis, “A hierarchical 3d gaussian representation for real-time rendering of very large datasets,” ACM Trans. Graph. , vol. 43, no. 4, pp. 1–15, 2024

2024

Closest in time.

T. Suzuki, “Fed3dgs: Scalable 3d gaussian splatting with federated learning,” arXiv preprint arXiv:2403.11460 , 2024

Original

2024

Closest in time.

Z. Wang and D. Xu, “Pygs: Large-scale scene representation with pyramidal 3d gaussian splatting,” arXiv preprint arXiv:2405.16829 , 2024

2024

Closest in time.

J. Lin, Z. Li, X. Tang, J. Liu, S. Liu, J. Liu, Y. Lu, X. Wu, S. Xu, Y. Yan et al. , “Vastgaussian: Vast 3d gaussians for large scene reconstruction,” in CVPR , 2024, pp. 5166–5175

2024

Closest in time.

J. C. Lee, D. Rho, X. Sun, J. H. Ko, and E. Park, “Compact 3d gaussian representation for radiance field,” in CVPR , 2024, pp. 21 719–21 728

2024

Closest in time.

T. Lu, M. Yu, L. Xu, Y. Xiangli, L. Wang, D. Lin, and B. Dai, “Scaffold-gs: Structured 3d gaussians for view-adaptive rendering,” in CVPR , 2024, pp. 20 654–20 664

2024

Closest in time.

X. Sun, J. C. Lee, D. Rho, J. H. Ko, U. Ali, and E. Park, “F-3dgs: Factorized coordinates and representations for 3d gaussian splatting,” arXiv preprint arXiv:2405.17083 , 2024

2024

Closest in time.

H. Wang, H. Zhu, T. He, R. Feng, J. Deng, J. Bian, and Z. Chen, “End-to-end rate-distortion optimized 3d gaussian representation,” arXiv preprint arXiv:2406.01597 , 2024

2024

Closest in time.

M. Niemeyer, F. Manhardt, M.-J. Rakotosaona, M. Oechsle, D. Duckworth, R. Gosula, K. Tateno, J. Bates, D. Kaeser, and F. Tombari, “Radsplat: Radiance field-informed gaussian splatting for robust real-time rendering with 900+ fps,” arXiv preprint arXiv:2403.13806 , 2024

2024

Closest in time.

W. Liu, T. Guan, B. Zhu, L. Ju, Z. Song, D. Li, Y. Wang, and W. Yang, “Efficientgs: Streamlining gaussian splatting for large-scale high-resolution scene representation,” arXiv preprint arXiv:2404.12777 , 2024

2024

Closest in time.

A. Hanson, A. Tu, V. Singla, M. Jayawardhana, M. Zwicker, and T. Goldstein, “Pup 3d-gs: Principled uncertainty pruning for 3d gaussian splatting,” arXiv preprint arXiv:2406.10219 , 2024

2024

Closest in time.

P. Papantonakis, G. Kopanas, B. Kerbl, A. Lanvin, and G. Drettakis, “Reducing the memory footprint of 3d gaussian splatting,” Proc. of the ACM on Computer Graphics and Interactive Techniques , vol. 7, no. 1, pp. 1–17, 2024

2024

Closest in time.

O. Mahmoud and M. Gendrin, “On reducing the number of gaussians for radiance field real-time rendering,” in 2024 ECTI DAMT & NCON . IEEE, 2024, pp. 259–264

2024

Closest in time.

S. Subhajyoti Mallick, R. Goel, B. Kerbl, F. V. Carrasco, M. Steinberger, and F. De La Torre, “Taming 3dgs: High-quality radiance fields with limited resources,” arXiv e-prints , pp. arXiv–2406, 2024

2024

Closest in time.

S. Niedermayr, J. Stumpfegger, and R. Westermann, “Compressed 3d gaussian splatting for accelerated novel view synthesis,” in CVPR , 2024, pp. 10 349–10 358

2024

Closest in time.

Y. Chen, Q. Wu, J. Cai, M. Harandi, and W. Lin, “Hac: Hash-grid assisted context for 3d gaussian splatting compression,” arXiv preprint arXiv:2403.14530 , 2024

2024

Closest in time.

X. Liu, X. Wu, P. Zhang, S. Wang, Z. Li, and S. Kwong, “Compgs: Efficient 3d scene representation via compressed gaussian splatting,” arXiv preprint arXiv:2404.09458 , 2024

2024

Closest in time.

Y. Wang, Z. Li, L. Guo, W. Yang, A. C. Kot, and B. Wen, “Contextgs: Compact 3d gaussian splatting with anchor level context model,” arXiv preprint arXiv:2405.20721 , 2024

2024

Closest in time.

J. Chung, J. Oh, and K. M. Lee, “Depth-regularized optimization for 3d gaussian splatting in few-shot images,” in CVPR , 2024, pp. 811–820

2024

Closest in time.

A. Paliwal, W. Ye, J. Xiong, D. Kotovenko, R. Ranjan, V. Chandra, and N. K. Kalantari, “Coherentgs: Sparse novel view synthesis with coherent 3d gaussians,” arXiv preprint arXiv:2403.19495 , 2024

2024

Closest in time.

Y. Chen, H. Xu, C. Zheng, B. Zhuang, M. Pollefeys, A. Geiger, T.-J. Cham, and J. Cai, “Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images,” arXiv preprint arXiv:2403.14627 , 2024

2024

Closest in time.

Z. Bao, G. Liao, K. Zhou, K. Liu, Q. Li, and G. Qiu, “Loopsparsegs: Loop based sparse-view friendly gaussian splatting,” arXiv preprint arXiv:2408.00254 , 2024

2024

Closest in time.

J. Zhang, J. Li, X. Yu, L. Huang, L. Gu, J. Zheng, and X. Bai, “Cor-gs: Sparse-view 3d gaussian splatting via co-regularization,” arXiv preprint arXiv:2405.12110 , 2024

2024

Closest in time.

J. Li, J. Zhang, X. Bai, J. Zheng, X. Ning, J. Zhou, and L. Gu, “Dngaussian: Optimizing sparse-view 3d gaussian radiance fields with global-local depth normalization,” in CVPR , 2024, pp. 20 775–20 785

2024

Closest in time.

Z. Fan, W. Cong, K. Wen, K. Wang, J. Zhang, X. Ding, D. Xu, B. Ivanovic, M. Pavone, G. Pavlakos et al. , “Instantsplat: Unbounded sparse-view pose-free gaussian splatting in 40 seconds,” arXiv preprint arXiv:2403.20309 , 2024

2024

Closest in time.

A. Adkins, T. Chen, and J. Biswas, “Obvi-slam: Long-term object-visual slam,” RA-L , 2024

2024

Closest in time.

Y. Wu, L. Pan, W. Wu, G. Wang, Y. Miao, and H. Wang, “Rl-gsbridge: 3d gaussian splatting based real2sim2real method for robotic manipulation learning,” arXiv:2409.20291 , 2024

2024

Closest in time.

T. Zhang, H.-X. Yu, R. Wu, B. Y. Feng, C. Zheng, N. Snavely, J. Wu, and W. T. Freeman, “Physdreamer: Physics-based interaction with 3d objects via video generation,” arXiv preprint arXiv:2404.13026 , 2024

2024

Closest in time.