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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Realistic Rain & Thunder Sounds V4.5 Ets2 1.40 < HD • 480p >

The mod specifically enhances how you experience a storm based on your perspective:

Watch this mod in action to see how it changes the atmospheric soundscape during a storm:

: It includes around 20 high-quality thunder samples that feature a realistic delay (1 to 5 seconds) after a lightning flash, simulating the real-world distance between light and sound.

A key feature of the mod for ETS2 1.40 is its dynamic interior and exterior audio transitions powered by the FMOD Sound Engine .

: Audio transitions (levels and fading) change dynamically based on the actual intensity of the rainfall outside.


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

The mod specifically enhances how you experience a storm based on your perspective:

Watch this mod in action to see how it changes the atmospheric soundscape during a storm:

: It includes around 20 high-quality thunder samples that feature a realistic delay (1 to 5 seconds) after a lightning flash, simulating the real-world distance between light and sound.

A key feature of the mod for ETS2 1.40 is its dynamic interior and exterior audio transitions powered by the FMOD Sound Engine .

: Audio transitions (levels and fading) change dynamically based on the actual intensity of the rainfall outside.


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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