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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SLUI.EXE is a legitimate executable file that is part of the Windows operating system. It is used to activate Windows and validate the operating system. However, some users may need to download SLUI.EXE for Windows 7 32-bit to 64-bit due to various reasons such as system crashes, corrupted files, or upgrading from a 32-bit to a 64-bit version.

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

SLUI.EXE is a legitimate executable file that is part of the Windows operating system. It is used to activate Windows and validate the operating system. However, some users may need to download SLUI.EXE for Windows 7 32-bit to 64-bit due to various reasons such as system crashes, corrupted files, or upgrading from a 32-bit to a 64-bit version.

Downloading SLUI.EXE for Windows 7 32-bit to 64-bit requires caution and attention to detail. Always prioritize official sources and trusted websites to avoid malware and ensure system safety. If you're experiencing issues with SLUI.EXE, try running the System File Checker tool or seeking assistance from Microsoft support. download sluiexe windows 7 32bit to 64bit 38 top

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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