The paper presents a novel SLAM system that uses 3D Gaussian Splatting (3DGS) as the sole underlying scene representation. This enables high-fidelity 3D reconstruction, even from monocular input, by leveraging the continuous and differentiable nature of the Gaussian representation.
Key highlights:
The system maintains a 3D Gaussian map of the scene, continuously optimizing the Gaussian parameters to represent the observed geometry and appearance. Camera poses are optimized by direct alignment against the 3D Gaussian map, without the need for explicit depth estimation or other pre-trained components.
The authors introduce several key innovations to enable this approach, including analytic Jacobians for efficient camera pose optimization, geometric regularization of the Gaussian shapes, and a resource allocation and pruning method to maintain a clean and consistent geometric representation.
Extensive evaluations on both monocular and RGB-D datasets demonstrate the system's ability to achieve state-of-the-art performance in camera tracking, mapping, and novel view synthesis, while offering significantly faster rendering speeds compared to other methods.
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by Hidenobu Mat... о arxiv.org 04-16-2024
https://arxiv.org/pdf/2312.06741.pdfГлибші Запити