OpenSfM is an open-source Structure from Motion (SfM) library developed by Mapillary. It's designed to reconstruct 3D scenes from multiple 2D images, providing tools for feature detection, matching, and bundle adjustment. OpenSfM is particularly useful for photogrammetry, 3D reconstruction, and computer vision applications.

Pros

• Highly customizable and extensible, allowing users to modify and adapt the pipeline to their specific needs
• Supports various input formats and camera models, making it versatile for different types of projects
• Includes advanced features like GPS data integration and distributed processing for large datasets
• Well-documented and actively maintained by the community

Cons

• Steep learning curve for beginners due to its complex nature and numerous configuration options
• Computationally intensive, especially for large datasets, which may require significant processing time and resources
• Limited built-in visualization tools, often requiring additional software for viewing results
• Dependency management can be challenging, particularly on Windows systems

Code Examples

1. Importing and initializing OpenSfM:

import opensfm
from opensfm import dataset

# Create a dataset
data = dataset.DataSet('/path/to/dataset')

2. Running the SfM pipeline:

# Run feature detection and matching
opensfm.commands.extract_metadata.run_dataset(data)
opensfm.commands.detect_features.run_dataset(data)
opensfm.commands.match_features.run_dataset(data)

# Run reconstruction
opensfm.commands.reconstruct.run_dataset(data)

3. Accessing reconstruction results:

# Load reconstruction
reconstruction = data.load_reconstruction()

# Access camera poses and 3D points
for shot in reconstruction[0].shots.values():
    print(f"Camera position: {shot.pose.get_origin()}")

for point in reconstruction[0].points.values():
    print(f"3D point: {point.coordinates}")

Getting Started

1. Install OpenSfM:

git clone https://github.com/mapillary/OpenSfM.git
cd OpenSfM
pip install -r requirements.txt
python setup.py install

2. Prepare your dataset:

   • Create a directory for your project
   • Place your images in an images subdirectory
   • Create a config.yaml file with desired settings

3. Run the SfM pipeline:

bin/opensfm_run_all /path/to/dataset

4. View results:
   • Use external software like Meshlab to visualize the 3D reconstruction
   • Check the reconstruction.json file for detailed output