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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Vintage Lolitas (29) Mp4 -

The phrase refers to a specific digital file name that has gained notoriety within internet subcultures, often associated with shock sites, "screamer" videos, or bait-and-switch memes. Context and Origin

: A precursor to "Rickrolling," where the file actually contains unrelated, often absurd, or humorous footage. The "Vintage" Misnomer

Ultimately, this file name is a relic of —a digital "urban legend" that highlights the risks and the chaotic nature of early online exploration.


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

The phrase refers to a specific digital file name that has gained notoriety within internet subcultures, often associated with shock sites, "screamer" videos, or bait-and-switch memes. Context and Origin

: A precursor to "Rickrolling," where the file actually contains unrelated, often absurd, or humorous footage. The "Vintage" Misnomer

Ultimately, this file name is a relic of —a digital "urban legend" that highlights the risks and the chaotic nature of early online exploration.


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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