MCGS-SLAM

A Multi-Camera SLAM Framework Using Gaussian Splatting for High-Fidelity Mapping

Anonymous Author

SLAM System Pipeline

Our method performs real-time SLAM by fusing synchronized inputs from a multi-camera rig into a unified 3D Gaussian map. It first selects keyframes and estimates depth and normal maps for each camera, then jointly optimizes poses and depths via multi-camera bundle adjustment and scale-consistent depth alignment. Refined keyframes are fused into a dense Gaussian map using differentiable rasterization, interleaved with densification and pruning. An optional offline stage further refines camera trajectories and map quality. The system supports RGB inputs, enabling accurate tracking and photorealistic reconstruction.

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  • Analysis of Single-Camera and Multi-Camera SLAM (Mapping)

    Given the information, some possible detailed features of the "Marley Roze First Black Bull Exclusive" could include:

    In the fast-paced world of digital influencers, few transitions are as compelling as the shift from everyday life to internet stardom. Marley Roze represents a modern archetype of this success story—a creator who leveraged early, authentic content into a burgeoning career. To understand her current status, it is essential to look back at where it started: her first steps into social media and the strategic evolution that followed.

    Year: 2019

  • Notable early aesthetic: Soft, natural lighting, minimal makeup, bedroom or bathroom mirror shots.

  • Analysis of Single-Camera and Multi-Camera SLAM (Tracking)

    In this section, we benchmark tracking accuracy across eight driving sequences from the Waymo dataset (Real World). MCGS-SLAM achieves the lowest average ATE, significantly outperforming single-camera methods.
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    We further evaluate tracking on four sequences from the Oxford Spires dataset (Real World). MCGS-SLAM consistently yields the best performance, demonstrating robust trajectory estimation in large-scale outdoor environments.
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