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)

Dedicated channels for cartoons and 24/7 Quran recitations.

Access to major regional sports networks and international matches. Download Arabic m3u

the M3U file or copy the playlist URL provided below. Dedicated channels for cartoons and 24/7 Quran recitations

your preferred player (VLC, IPTV Smarters, or Perfect Player). VLC Media Player

Note: For the best experience, we recommend using a stable high-speed internet connection.

Our offers a seamless way to stream your favorite contentā€”from live news and sports to the latest Ramadan series and classic cinema. Whether you are using a Smart TV, VLC Media Player, or a mobile IPTV app, these links are optimized for high-quality streaming and minimal buffering. Whatā€™s Included in the Arabic M3U?

The biggest channels from MBC, Rotana, and OSN.

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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