MCGS-SLAM

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

Anonymous Author
Code and data [Available Upon Acceptance]

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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Alphabet_lore_but_every_letter_is_reverse_wrong... Now

Scenes from the original series played backward, often creating a "Benjamin Button" effect where adult letters transform into children.

Letters may transform into the "wrong" characters, such as X transforming into C or P into K. Overview of Alphabet Lore

If you are writing a paper on this topic, it is helpful to understand the source material. is a narrative-driven animated series on YouTube where letters of the alphabet are personified characters. Alphabet Lore (Web Animation) - TV Tropes

The original sound effects and voices are replaced with mismatched, distorted, or high-pitched "wrong" voices.

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

Scenes from the original series played backward, often creating a "Benjamin Button" effect where adult letters transform into children.

Letters may transform into the "wrong" characters, such as X transforming into C or P into K. Overview of Alphabet Lore

If you are writing a paper on this topic, it is helpful to understand the source material. is a narrative-driven animated series on YouTube where letters of the alphabet are personified characters. Alphabet Lore (Web Animation) - TV Tropes

The original sound effects and voices are replaced with mismatched, distorted, or high-pitched "wrong" voices.

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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