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README.md
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license: mit
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Fastmap evaluation suite.
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## Images
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We performed some light image curation
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## Databases
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Unless specified below, all databases `.db` files are produced with
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```
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colmap feature_extractor --ImageReader.single_camera 1 --ImageReader.camera_model SIMPLE_RADIAL --image_path {imgdir} --database_path {db_fname}
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colmap exhaustive_matcher --database_path {db_fname}
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```
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We have added our gt convertion script `convert_gt.py` to this repo as a reference.
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---
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license: mit
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# Fastmap evaluation suite.
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You only need the databases to run fastmap. Download the images if you want to produce colored point cloud.
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Download the subset of data you want to your local directory.
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```bash
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huggingface-cli download whc/fastmap_sfm --repo-type dataset --local-dir ./ --include 'databases/*' 'ground_truths/*'
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```
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or use the python interface
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```python
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from huggingface_hub import hf_hub_download, snapshot_download
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snapshot_download(
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repo_id="whc/fastmap_sfm", repo_type='dataset',
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local_dir="./",
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allow_patterns=["ground_truths/*",],
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max_workers=8
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)
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```
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## Images
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We performed some light image curation
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## Databases
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Unless specified below, all databases `.db` files are produced with
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```bash
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colmap feature_extractor --ImageReader.single_camera 1 --ImageReader.camera_model SIMPLE_RADIAL --image_path {imgdir} --database_path {db_fname}
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colmap exhaustive_matcher --database_path {db_fname}
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```
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We have added our gt convertion script `convert_gt.py` to this repo as a reference.
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## glomap/colmap container
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We provide a singularity container `glomap_250121.sif` in the repo, and a docker container [here](https://hub.docker.com/r/haochenw/glomap/tags).
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The Dockerfile is printed in the dockerhub overview.
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