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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Uдџur Iеџд±lak Beni Bг¶yle Kabul Et Apr 2026

Decades later, it remains a staple in his live performances, often resulting in massive sing-alongs.

The track is a hallmark of the and Özgün Müzik genres.

While traditional in spirit, the production incorporates subtle modern synthesizers and percussion common in 90s Turkish music. 🌍 Cultural Impact

It resonates with anyone who has felt judged or misunderstood by society.

Işılak’s deep, resonant voice conveys a sense of weariness and wisdom.

For many listeners, the song became an anthem for the and the "common man."

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

Decades later, it remains a staple in his live performances, often resulting in massive sing-alongs.

The track is a hallmark of the and Özgün Müzik genres.

While traditional in spirit, the production incorporates subtle modern synthesizers and percussion common in 90s Turkish music. 🌍 Cultural Impact

It resonates with anyone who has felt judged or misunderstood by society.

Işılak’s deep, resonant voice conveys a sense of weariness and wisdom.

For many listeners, the song became an anthem for the and the "common man."

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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