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

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For an interesting piece that fits the spirit of a "Cabinet of Curiosities" (Curiosa), here are a few curated options based on popular, unique, and conversation-starting items: Do you have a preference for or classic/elegant styles

: A set of 17 pieces of vintage brass miniatures in a small display cabinet, ideal for a "cabinet within a cabinet" look, available on Etsy . For an interesting piece that fits the spirit

: A handcrafted cream and gold leaf Sicilian head pot, perfect for holding small treasures or serving as a striking vase.

For a truly unique, dramatic piece, the is considered a quintessential "Curiosa" object. If you want to refine this list, let me know:

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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