SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM
By Nikhil Varma Keetha, Jay Karhade, Krishna Murthy Jatavallabhula, Gengshan Yang, Sebastian A. Scherer, Deva Ramanan, Jonathon Luiten. Computer Vision and Pattern Recognition, 2023
653 citations recorded by the source corpus.
Dense simultaneous localization and mapping (SLAM) is crucial for robotics and augmented reality applications. However, current methods are often hampered by the non-volumetric or implicit way they represent a scene. This work introduces SplaTAM, an approach that, for the first time, leverages explicit volumetric representations, i.e., 3D Gaussians, to enable high-fidelity reconstruction from a single unposed RGB-D camera, surpassing the capabilities of existing methods. SplaTAM employs a simple online tracking and mapping system tailored to the underlying Gaussian representation. It utilizes a silhouette mask to elegantly capture the presence of scene density. This combination enables several benefits over prior representations, including fast rendering and dense optimization, quickly determining if areas have been previously mapped, and structured map expansion by adding more Gaussians. Extensive experiments show that SplaTAM achieves up to 2 x superior performance in camera pose estimation, map construction, and novel-view synthesis over existing methods, paving the way for more immersive high-fidelity SLAM applications.
Subfields: Aerospace Engineering, Environmental Engineering, Geology
View Paper: https://www.semanticscholar.org/paper/a6c92dd8c537bc4a0c91cef8558e4a4f25416091
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Citations in the corpus
- Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians (2024, background): “…rapidly expanded 3D-GS into diverse downstream applications beyond static 3D reconstruction, sparking a surge of extended applications to 3D generative modeling [31]–[33], physical simulation [13], [34], dynamic modeling [35]–[37], SLAMs [38], [39], and autonomous driving scenes [10]–[12], etc.”
- Recent advances in 3D Gaussian splatting (2024, positive): “To avoid duplicate densifi-cation, SplaTAM [116] uses view-independent colors for Gaussians and creates a densification mask to determine whether a pixel in a new frame needs densification by considering current Gaussians and the captured depth of the new frame.”
- A Survey on 3D Gaussian Splatting (2024, neutral): “For head avatars [25, 39, 117, 178, 188, 191, 245, 297, 307], the emphasis shifts to modeling intricate facial expressions, fine-grained geometry (e.g., wrinkles, hair [144]), and dynamic speech-driven animations.”