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 System

This experiment on the Waymo Open Dataset (Real World) demonstrates the effectiveness of our Multi-Camera Gaussian Splatting SLAM system. We evaluate the 3D mapping performance using three individual cameras, Front, Front-Left, and Front-Right, and compare these single-camera reconstructions against the Multi-Camera SLAM results.

The comparison highlights that the Multi-Camera SLAM leverages complementary viewpoints, providing more complete and geometrically consistent 3D reconstructions. In contrast, single-camera setups are prone to occlusions and limited fields of view, resulting in incomplete or distorted geometry. Our approach effectively fuses information from all three perspectives, achieving superior scene coverage and depth accuracy.

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The Indonesian entertainment industry has a long history, with traditional forms of entertainment such as wayang (shadow puppetry) and ludruk (traditional theater) dating back to the 19th century. However, the modern entertainment industry in Indonesia began to take shape in the 1970s with the emergence of film and television. The industry experienced significant growth in the 1990s with the introduction of private television stations and the rise of Indonesian pop music.

"The Rise of Indonesian Entertainment: A Study on Popular Videos and Trends in the Digital Age" The Indonesian entertainment industry has a long history,

Indonesian entertainment and popular videos have experienced significant growth in recent years, driven by the increasing popularity of online platforms and social media. The analysis of popular videos revealed a diverse range of genres and themes, including music, comedy, film, and vlogs. The success of Indonesian entertainment videos can be attributed to factors such as digitalization, cultural relevance, creative storytelling, and influencer marketing. As the Indonesian entertainment industry continues to evolve, it is likely to remain a significant player in the global digital entertainment landscape. "The Rise of Indonesian Entertainment: A Study on

Indonesia, with its population of over 270 million people, has become a significant market for entertainment and digital content in Southeast Asia. The country's entertainment industry has experienced rapid growth in recent years, driven by the increasing popularity of online platforms and social media. This paper aims to explore the current trends and popular videos in Indonesian entertainment, as well as the factors contributing to their success. and characteristics of the videos

This study used a qualitative approach, analyzing popular videos and trends in Indonesian entertainment. A total of 100 popular videos from Indonesia were selected for analysis, including music videos, movie trailers, and comedy sketches. The videos were chosen based on their viewership and engagement on YouTube and other social media platforms. The analysis focused on the themes, genres, and characteristics of the videos, as well as the audience's response to them.

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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