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)

Extract all images from a PDF simultaneously into individual, high-quality files.

Improved recognition for scanned documents with new interface support for Italian and Portuguese. How to Install and Activate

Get the installation file from the official Investintech site. download able2extract professional 17

Instantly extract raw data from PDF forms into CSV files for easy data management.

Open the downloaded file and follow the on-screen prompts to choose your installation directory. Extract all images from a PDF simultaneously into

Able2Extract 17 is a comprehensive PDF suite designed for advanced data extraction and document management. New highlights in this version include:

Upon launching, select "Activate a License" and enter the PIN received at purchase to unlock the full version. System Requirements Instantly extract raw data from PDF forms into

Able2Extract Professional 17 is cross-platform, supporting Windows, macOS, and Linux: New Able2Extract Professional 17 Is Here! - PDF converter

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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