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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Atkpetites130922mattieborderstoysxxx108 Work 〈2026 Update〉

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

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When it comes to choosing toys for children, parents and caregivers want to ensure that they are not only fun and engaging but also safe and suitable for their little ones. For kids who are petite or have smaller hands, finding toys that are proportional to their size can be a challenge. This is where petite toys and playsets come in – designed specifically for young children, these toys offer a world of imaginative play and learning opportunities. atkpetites130922mattieborderstoysxxx108 work

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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