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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Kera Bear Apr 2026

The Kera Bear is an extraordinary creature that deserves our attention, respect, and protection. As we continue to learn more about this enigmatic animal, we are reminded of the importance of preserving our natural world and the delicate balance of ecosystems. By supporting conservation efforts and spreading awareness about the Kera Bear, we can help ensure the long-term survival of this incredible species.

Deep in the heart of the forest, a mysterious and elusive creature roams, shrouded in secrecy and intrigue. Meet the Kera Bear, a fascinating and enigmatic animal that has captured the imagination of wildlife enthusiasts and researchers alike. In this article, we'll delve into the world of the Kera Bear, exploring its habitat, behavior, and characteristics, as well as the challenges and threats it faces in the wild. kera bear

Unfortunately, the Kera Bear is listed as a vulnerable species due to habitat loss, fragmentation, and poaching. The destruction of their natural habitat has reduced their population significantly, and human-Kera Bear conflicts have increased as a result. Conservation efforts are underway to protect the Kera Bear and its habitat, but more needs to be done to ensure the long-term survival of this incredible creature. The Kera Bear is an extraordinary creature that

Kera Bears are found in the temperate forests of a specific region, where they inhabit areas with dense vegetation, abundant food sources, and access to water. Their habitat ranges from mountainous terrain to low-lying valleys, and they are known to migrate seasonally in search of food and shelter. Despite their adaptability, Kera Bears are highly dependent on their habitat, and any disruption to their environment can have severe consequences for their survival. Deep in the heart of the forest, a

The Kera Bear (also known as the Kerabear or Kera) is a large, carnivorous mammal that inhabits the dense forests of a remote region. Belonging to the family of bears, it is characterized by its distinctive physical features, including a stocky build, thick fur, and a short, snubbed snout. Adult Kera Bears typically weigh between 300-500 kg (660-1100 lbs) and reach lengths of up to 2.5 meters (8.2 feet).

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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