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Quickstart

Reconstructing the Example Dataset

An example dataset is available at data/berlin. Reconstruct it by running:

bin/opensfm_run_all data/berlin

This runs the entire SfM pipeline and produces data/berlin/reconstruction.meshed.json as output.

Running in Docker

First, build the OpenSfM Docker image as described in building.

Start a Docker container, mounting the data/ folder:

docker run -it -p 8080:8080 -v ${PWD}/data/:/data/ opensfm.ubuntu24 /bin/bash

Inside the container, run the reconstruction:

bin/opensfm_run_all /data/berlin/

When done, exit with Ctrl+d. The model will be available in the data/ directory.

Viewer

A web-based viewer is included. Start it with:

python viewer/server.py -d path/to/dataset

Dense Point Clouds

For a denser point cloud:

bin/opensfm undistort data/berlin
bin/opensfm dense_clustering data/berlin
bin/opensfm compute_depthmaps data/berlin
bin/opensfm fuse_depthmaps data/berlin
bin/opensfm dense_merging data/berlin

This runs dense multi-view stereo and produces a dense point cloud at data/berlin/undistorted/depthmaps/fused.ply (along with a mesh.ply, a DSM and an orthophoto). Visualize the cloud with MeshLab or any viewer supporting PLY files.

Reconstructing Your Own Images

  1. Put images in data/DATASET_NAME/images/
  2. Optionally copy data/berlin/config.yaml to data/DATASET_NAME/config.yaml
  3. Run the full pipeline:
bin/opensfm_run_all data/DATASET_NAME

Or run steps individually (see pipeline commands):

bin/opensfm extract_metadata data/DATASET_NAME
bin/opensfm detect_features  data/DATASET_NAME
bin/opensfm match_features   data/DATASET_NAME
bin/opensfm create_tracks    data/DATASET_NAME
bin/opensfm reconstruct      data/DATASET_NAME

See dataset structure for the expected folder layout and configuration reference for tuning options.