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)

Download the .exe file from the Orange website. Run the file, agree to the terms, and follow the prompts. The installer typically includes Miniconda to manage Python dependencies automatically.

Orange is compatible with all major operating systems and offers multiple installation methods to suit your technical environment. 1. Windows Installation orange data mining tool download

Orange is a powerful, open-source platform that simplifies data science by replacing complex coding with an intuitive, visual workflow. Whether you are a student, researcher, or professional, this guide provides everything you need for an and successful installation. Quick Download Links Download the

You can download the latest version of Orange (currently version as of April 2026) directly from the official site: Official Orange Download Page GitHub Repository (Source Code) Step-by-Step Installation Guide Orange is compatible with all major operating systems

Open your terminal and run: pip install PyQt6 PyQt6-WebEngine orange3

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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