DGS-SLAM

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DGS-SLAM: Gaussian Splatting SLAM in Dynamic Environment

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# DGS-SLAM: Gaussian Splatting SLAM in Dynamic Environments

[![arXiv](https://img.shields.io/badge/arXiv-2411.10722-B31B1B.svg)](https://arxiv.org/abs/2411.10722)

## Abstract

Dynamic Gaussian Splatting SLAM (DGS-SLAM) is the first dynamic SLAM framework built on the foundation of **Gaussian Splatting**. While recent advancements in dense SLAM have leveraged Gaussian Splatting to enhance scene representation, most approaches assume a static environment, making them vulnerable to photometric and geometric inconsistencies caused by dynamic objects.

DGS-SLAM addresses these challenges by integrating a **robust filtering process** into the Gaussian Splatting pipeline. This framework effectively handles dynamic objects throughout the entire SLAM pipeline, including **Gaussian insertion** and **keyframe selection**, to achieve robust performance in dynamic environments.

<!-- ## 🔥 Key Features

- **Dynamic Object Filtering**  
  Introduces a robust mask generation method to enforce photometric consistency across keyframes, reducing segmentation noise and artifacts such as shadows.

- **Loop-Aware Optimization**  
  Proposes a loop-aware window selection mechanism that detects and optimizes loops using unique keyframe IDs of 3D Gaussians.

- **State-of-the-Art Results**  
  Achieves superior performance in **camera tracking** and **novel view synthesis** on dynamic SLAM benchmarks.

[➡️ Explore the Code](https://github.com/kmk97/DGS-SLAM) -->

---

## 📜 Pipeline Overview

The DGS-SLAM pipeline incorporates Gaussian Splatting, robust filtering, keyframe management, and loop-aware optimization. Below is a visual representation of the process:

![Pipeline](https://raw.githubusercontent.com/kmk97/DGS-SLAM/main/assets/overview.png)

---

## 🎥 Results

Check out the example videos below:

[![Watch Video](https://img.youtube.com/vi/Mq3qZTTcN3E/maxresdefault.jpg)](https://youtu.be/Mq3qZTTcN3E)  
*(Click the image to watch the video on YouTube)*

---

<!-- ## 🚀 Getting Started

### Installation

```bash
# Clone the repository
git clone https://github.com/kmk97/DGS-SLAM.git
cd DGS-SLAM

# Install dependencies
pip install -r requirements.txt -->

DGS-SLAM

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