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license: mit
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---
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license: mit
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---
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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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For nerf-osr (i.e. those with prefix `nosr_`), `dploy_house4`, urbanscene (`urbn_`), eyeful tower (`eft_`),
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we use `--ImageReader.single_camera 0`.
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For eth3d MVS (`eth3d_dslr_`), we populate the databases with author provided ground truth camera intrinsics.
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This is to keep the eval protocol consistent with Glomap. see [issue 96](https://github.com/colmap/glomap/issues/96).
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## Ground truths
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We use two types of ground-truth formats. The first is colmap `.bin` output.
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Secondly, for datasets whose ground truths come in non-standard format, we verify and convert them to a white-box json file each containing a list of `{fname: str, c2w: list[float]}`.
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The `c2w` matrix is in OpenCV convention.
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