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This paper presents a novel system designed for 3D mapping and visual relocalization using 3D Gaussian Splatting.
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2021
Cited alongside, same era.
B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis, “3D Gaussian Splatting for Real-Time Radiance Field Rendering,” vol. 42, no. 4, p. 1
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Y. Liao, J. Xie, and A. Geiger, “KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D,” vol. 45, no. 3, pp. 3292–3310
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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.”
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V. Yugay, Y. Li, T. Gevers, and M. R. Oswald. Gaussian-SLAM: Photo-realistic Dense SLAM with Gaussian Splatting
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H. Matsuki, R. Murai, P. H. J. Kelly, and A. J. Davison. Gaussian Splatting SLAM
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M. Li, S. Liu, H. Zhou, G. Zhu, N. Cheng, and H. Wang. SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM
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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
Cited in the paper.
P. Jiang and S. Saripalli, “Contrastive Learning of Features between Images and LiDAR,” in 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) , pp. 411–417
2022
Later among the works it cites.
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