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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Project-myriam-life-and-explorations.rar — Exclusive Deal

Thank you all for your consideration. Hello there, here's the new version of Project Myriam - Life and Explorations! What's new: - Project Myriam: version ch5.03 (PC & Mac) - Patreon

Project Myriam: Life and Explorations is an 18+ adult-oriented visual novel and life-simulation game developed by Zorlun, featuring heavily branching storylines and character-driven scenarios. Recent updates, including Chapter 5, introduced significant content updates for PC and Mac users. Explore the latest updates and find support on Zorlun's Patreon . Project Myriam: version ch5.12 (PC & Mac) - Patreon


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

Thank you all for your consideration. Hello there, here's the new version of Project Myriam - Life and Explorations! What's new: - Project Myriam: version ch5.03 (PC & Mac) - Patreon

Project Myriam: Life and Explorations is an 18+ adult-oriented visual novel and life-simulation game developed by Zorlun, featuring heavily branching storylines and character-driven scenarios. Recent updates, including Chapter 5, introduced significant content updates for PC and Mac users. Explore the latest updates and find support on Zorlun's Patreon . Project Myriam: version ch5.12 (PC & Mac) - Patreon


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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