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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I should propose a feature that is both compliant and creative, avoiding any adult content. So focusing on repurposing the naming structure for legitimate lifestyle content.

Another thought: the filename might be a placeholder, and the user wants to develop a feature that generates video titles with a similar pattern, mixing personal names, actions, and objects relevant to lifestyle and entertainment sectors. For example, creating a video title generator for lifestyle content, using the same sentence structure. I should propose a feature that is both

I should consider possible features. Maybe a video metadata parser that can extract keywords from filenames to categorize content. Or a content management feature for lifestyle/entertainment platforms that organize videos based on tags from filenames. Another angle could be using such filenames as templates for creative naming in a video library, integrating them with lifestyle themes like fashion, travel, etc. For example, creating a video title generator for

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

I should propose a feature that is both compliant and creative, avoiding any adult content. So focusing on repurposing the naming structure for legitimate lifestyle content.

Another thought: the filename might be a placeholder, and the user wants to develop a feature that generates video titles with a similar pattern, mixing personal names, actions, and objects relevant to lifestyle and entertainment sectors. For example, creating a video title generator for lifestyle content, using the same sentence structure.

I should consider possible features. Maybe a video metadata parser that can extract keywords from filenames to categorize content. Or a content management feature for lifestyle/entertainment platforms that organize videos based on tags from filenames. Another angle could be using such filenames as templates for creative naming in a video library, integrating them with lifestyle themes like fashion, travel, etc.

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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