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.gitattributes

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 # Video files - compressed
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README.md

@@ -1,3 +1,123 @@
 ---
-license: mit
+pretty_name: COLMAP Testing Dataset
+license: other
+task_categories:
+  - image-to-3d
+  - depth-estimation
+tags:
+  - structure-from-motion
+  - multi-view-stereo
+  - colmap
+  - sfm
+  - mvs
+  - 3d-reconstruction
+  - photogrammetry
+  - eth3d
+size_categories:
+  - n<1K
 ---
+
+# COLMAP Testing Dataset
+
+A small, ready-to-run collection of multi-view image scenes for testing and
+benchmarking the [COLMAP](https://colmap.github.io/) Structure-from-Motion (SfM)
+and Multi-View Stereo (MVS) pipeline — used here to validate a macOS / Apple
+Silicon (Metal) build of COLMAP, but useful for any SfM/MVS work.
+
+It bundles two well-known families of scenes, each already laid out in the
+directory structure COLMAP expects, so you can point COLMAP at a folder and run
+the pipeline end-to-end with no preprocessing.
+
+## What's inside
+
+| Scene | Family | Images | Resolution | Ground truth provided | Size |
+|-------|--------|-------:|------------|------------------------|-----:|
+| `courtyard` | ETH3D high-res DSLR | 38 | 6048×4032 | calibration + sparse (COLMAP txt) | 414 MB |
+| `electro` | ETH3D high-res DSLR | 45 | 6048×4032 | calibration + sparse (COLMAP txt) | 411 MB |
+| `kicker` | ETH3D high-res DSLR | 31 | 6048×4032 | calibration + sparse (COLMAP txt) | 394 MB |
+| `relief` | ETH3D high-res DSLR | 31 | 6048×4032 | calibration + sparse (COLMAP txt) | 281 MB |
+| `terrains` | ETH3D high-res DSLR | 42 | 6048×4032 | calibration + sparse (COLMAP txt) | 350 MB |
+| `gerrard-hall` | COLMAP example | 100 | ~3072×2304 | sparse model + prebuilt `database.db` (158 MB) | 1.0 GB |
+| `south-building` | COLMAP example | 128 | ~3072×2304 | sparse model + prebuilt `database.db` (211 MB) | 466 MB |
+
+Total: **7 scenes, 415 images, ~3.2 GB.**
+
+## Directory layout
+
+Two layouts, depending on the family:
+
+**ETH3D scenes** (`courtyard`, `electro`, `kicker`, `relief`, `terrains`):
+
+```
+<scene>/
+├── images/
+│   └── dslr_images/           # the input JPGs (6048×4032)
+└── dslr_calibration_jpg/      # ground-truth, COLMAP text format
+    ├── cameras.txt            # THIN_PRISM_FISHEYE intrinsics (4 cameras)
+    ├── images.txt             # GT camera poses (2 lines per image)
+    └── points3D.txt           # GT sparse 3D points
+```
+
+**COLMAP example scenes** (`gerrard-hall`, `south-building`):
+
+```
+<scene>/
+├── images/                    # the input JPGs (flat)
+├── sparse/                    # reference sparse reconstruction (COLMAP text)
+│   ├── cameras.txt
+│   ├── images.txt
+│   └── points3D.txt
+└── database.db                # prebuilt COLMAP database (features + matches)
+```
+
+## Quick start with COLMAP
+
+Sparse reconstruction from scratch (works for any scene):
+
+```bash
+SCENE=south-building            # or courtyard, electro, ...
+IMG=$SCENE/images               # ETH3D: $SCENE/images/dslr_images
+
+colmap feature_extractor --image_path "$IMG" --database_path db.db
+colmap exhaustive_matcher  --database_path db.db
+colmap mapper --image_path "$IMG" --database_path db.db --output_path sparse
+colmap model_analyzer --path sparse/0
+```
+
+The COLMAP example scenes ship a prebuilt `database.db` (features already
+extracted and matched), so you can skip straight to mapping:
+
+```bash
+colmap mapper --image_path south-building/images \
+  --database_path south-building/database.db --output_path sparse
+```
+
+Dense MVS (after sparse, with a Metal or CUDA build):
+
+```bash
+colmap image_undistorter --image_path "$IMG" --input_path sparse/0 --output_path dense
+colmap patch_match_stereo --workspace_path dense
+colmap stereo_fusion --workspace_path dense --output_path dense/fused.ply
+```
+
+## Provenance, licensing & citation
+
+These scenes are **redistributed for testing convenience**; they are not
+original to this dataset. Please respect and cite the original sources, and
+consult their terms before any non-testing use.
+
+- **ETH3D scenes** (`courtyard`, `electro`, `kicker`, `relief`, `terrains`) come
+  from the **ETH3D multi-view stereo benchmark** (high-resolution DSLR subset),
+  Schöps *et al.*, *"A Multi-View Stereo Benchmark with High-Resolution Images
+  and Multi-Camera Videos"*, CVPR 2017 — <https://www.eth3d.net/>. The
+  laser-scan ground-truth surface is **not** included here (only the
+  camera calibration and ground-truth sparse points).
+- **COLMAP example scenes** (`gerrard-hall`, `south-building`) are the standard
+  COLMAP example datasets, Schönberger & Frahm, *"Structure-from-Motion
+  Revisited"*, CVPR 2016 — <https://colmap.github.io/datasets.html>.
+
+`license: other` — licensing follows the original sources above, not a single
+blanket license.
+
+See **`llm.txt`** for a dense, machine-readable guide to the file formats and
+how to load/evaluate against the ground truth.

courtyard/dslr_calibration_jpg/cameras.txt

@@ -0,0 +1,7 @@
+# Camera list with one line of data per camera:
+#   CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]
+# Number of cameras: 4
+0 THIN_PRISM_FISHEYE 6048 4032 3411.42 3410.02 3041.29 2014.07 0.21047 0.21102 -5.36231e-06 0.00051541 -0.158023 0.406856 -8.46499e-05 0.000861313
+1 THIN_PRISM_FISHEYE 6048 4032 3409.58 3409.44 3036.34 2013.3 0.20962 0.217455 -0.000522558 0.000119474 -0.178909 0.418133 0.000258716 0.00133562
+2 THIN_PRISM_FISHEYE 6048 4032 3407.41 3408.08 3036.52 2014.63 0.216765 0.191408 -0.000859355 0.000291099 -0.154065 0.417123 0.000113426 0.00182649
+3 THIN_PRISM_FISHEYE 6048 4032 3408.35 3408.8 3034.42 2019.82 0.211673 0.208647 0.000531937 -0.000154306 -0.165688 0.4075 0.000489949 0.000281764

electro/dslr_calibration_jpg/cameras.txt

@@ -0,0 +1,9 @@
+# Camera list with one line of data per camera:
+#   CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]
+# Number of cameras: 6
+0 THIN_PRISM_FISHEYE 6048 4032 3408.91 3408.38 3037.08 2013.62 0.204422 0.237465 0.000589119 5.87063e-05 -0.209642 0.434556 0.00071821 -0.000137067
+1 THIN_PRISM_FISHEYE 6048 4032 3408.95 3408.5 3036.87 2012.87 0.209375 0.232522 0.00101428 8.27252e-05 -0.226781 0.461204 0.000557532 -0.000667222
+2 THIN_PRISM_FISHEYE 6048 4032 3404.55 3404.11 3038.26 2010.75 0.217681 0.191388 1.63869e-05 0.000604296 -0.146665 0.405993 -7.83548e-05 0.000277017
+3 THIN_PRISM_FISHEYE 6048 4032 3409.18 3408.84 3037.21 2015.3 0.203938 0.245656 0.000640945 -2.96877e-06 -0.233244 0.454785 0.000864363 -0.000298988
+4 THIN_PRISM_FISHEYE 6048 4032 3400.23 3404.6 3045.08 2011.29 0.220799 0.178643 0.00129795 0.00108593 -0.123804 0.408812 -0.00031356 -0.00242705
+5 THIN_PRISM_FISHEYE 6048 4032 3398.23 3396.19 3036.97 2012.36 0.229726 0.125479 0.000895368 -0.000558452 -0.00749247 0.310279 0.00167685 -7.40504e-05

gerrard-hall/sparse/cameras.txt

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+# Camera list with one line of data per camera:
+#   CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]
+# Number of cameras: 1
+1 OPENCV 5616 3744 3838.27 3837.22 2808 1872 -0.110339 0.079547 0.000116211 0.00029483

gerrard-hall/sparse/images.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:205e29b5e177e8029cfb4aa5e0a11238a3a1937add8e58f5b3bfb8cc651e7dbc
+size 20622366

kicker/dslr_calibration_jpg/cameras.txt

@@ -0,0 +1,4 @@
+# Camera list with one line of data per camera:
+#   CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]
+# Number of cameras: 1
+0 THIN_PRISM_FISHEYE 6048 4032 3410.34 3409.98 3039.41 2014.61 0.20987 0.216659 0.000220049 8.06366e-06 -0.180318 0.415527 0.000766344 0.000344008

llm.txt

@@ -0,0 +1,111 @@
+# COLMAP Testing Dataset — usage & data guide (for LLMs/agents and humans)
+
+Purpose: ready-to-run multi-view image scenes for testing/benchmarking the
+COLMAP SfM/MVS pipeline. 7 scenes, 415 images, ~3.2 GB, in two families with
+two different on-disk layouts. Everything is already in COLMAP's expected
+structure — no preprocessing needed.
+
+## Scenes
+
+ETH3D high-res DSLR (images at `<scene>/images/dslr_images/`, 6048x4032 JPG,
+ground-truth in `<scene>/dslr_calibration_jpg/`):
+- courtyard  (38 images)
+- electro    (45 images)
+- kicker     (31 images)
+- relief     (31 images)
+- terrains   (42 images)
+
+COLMAP example scenes (images at `<scene>/images/`, reference model in
+`<scene>/sparse/`, prebuilt `<scene>/database.db`):
+- gerrard-hall   (100 images, database.db 158 MB)
+- south-building (128 images, database.db 211 MB)
+
+## On-disk layout
+
+ETH3D:
+  <scene>/images/dslr_images/*.JPG          # inputs (24 MP)
+  <scene>/dslr_calibration_jpg/cameras.txt  # GT intrinsics (THIN_PRISM_FISHEYE)
+  <scene>/dslr_calibration_jpg/images.txt   # GT poses (2 lines per image)
+  <scene>/dslr_calibration_jpg/points3D.txt # GT sparse points
+
+COLMAP examples:
+  <scene>/images/*.JPG    # inputs
+  <scene>/sparse/{cameras.txt,images.txt,points3D.txt}  # reference sparse model
+  <scene>/database.db     # prebuilt SQLite DB: features + matches already done
+
+IMPORTANT path difference: ETH3D images live under `images/dslr_images`, COLMAP
+examples under `images`. Pass the correct `--image_path` accordingly.
+
+## COLMAP model file formats (what cameras/images/points3D mean)
+
+These are COLMAP's standard sparse-model text files (same schema everywhere).
+
+cameras.txt — one line per camera:
+  CAMERA_ID  MODEL  WIDTH  HEIGHT  PARAMS...
+  ETH3D uses MODEL=THIN_PRISM_FISHEYE with 12 params
+  (fx fy cx cy k1 k2 p1 p2 k3 k4 sx1 sy1). COLMAP example scenes use simpler
+  models (e.g. SIMPLE_RADIAL / PINHOLE) — read the file, don't assume.
+
+images.txt — TWO lines per image (this trips up naive parsers):
+  line 1:  IMAGE_ID  QW QX QY QZ  TX TY TZ  CAMERA_ID  NAME
+           (quaternion + translation = world-to-camera, i.e. cam_from_world)
+  line 2:  X Y POINT3D_ID  X Y POINT3D_ID ...  (2D keypoints; -1 = no 3D match)
+  So image count = (non-comment, non-blank lines) / 2.
+
+points3D.txt — one line per 3D point:
+  POINT3D_ID  X Y Z  R G B  ERROR  (IMAGE_ID POINT2D_IDX)...  (the track)
+
+Binary equivalents (cameras.bin/images.bin/points3D.bin) are interchangeable;
+convert with `colmap model_converter --output_type {BIN,TXT}`.
+
+## How to run COLMAP on a scene
+
+Set the image path per family, then:
+  SCENE=south-building;  IMG=$SCENE/images            # COLMAP examples
+  SCENE=relief;          IMG=$SCENE/images/dslr_images # ETH3D
+
+Sparse (from scratch):
+  colmap feature_extractor --image_path "$IMG" --database_path db.db
+  colmap exhaustive_matcher --database_path db.db        # small/medium scenes
+  colmap mapper --image_path "$IMG" --database_path db.db --output_path sparse
+  colmap model_analyzer --path sparse/0
+
+Reuse the prebuilt database (COLMAP example scenes only — skips extract+match):
+  colmap mapper --image_path south-building/images \
+    --database_path south-building/database.db --output_path sparse
+
+Dense MVS (needs a Metal or CUDA build; after sparse):
+  colmap image_undistorter --image_path "$IMG" --input_path sparse/0 --output_path dense
+  colmap patch_match_stereo --workspace_path dense
+  colmap stereo_fusion --workspace_path dense --output_path dense/fused.ply
+
+## Loading / evaluating against ground truth
+
+- Load a model in Python: `import pycolmap; rec = pycolmap.Reconstruction("relief/dslr_calibration_jpg")`
+  then `rec.cameras`, `rec.images` (each has `.cam_from_world`), `rec.points3D`.
+- The ETH3D `dslr_calibration_jpg` and the COLMAP `sparse` folders ARE valid
+  COLMAP models — open them directly to get GT/reference poses and points.
+- Typical SfM eval: run your pipeline, align your model to the GT model
+  (`colmap model_aligner` / pycolmap), then compare camera centers and rotations.
+
+## Choosing a scene
+
+- Fastest smoke test: relief or kicker (31 imgs each).
+- Larger / stress: gerrard-hall (100) or south-building (128).
+- Skip extraction+matching: the two COLMAP scenes ship database.db.
+- Distortion / high-res robustness: any ETH3D scene (24 MP, fisheye).
+- Pose-accuracy evaluation: any scene (all ship a GT/reference model).
+
+## Caveats
+
+- The ETH3D laser-scan ground-truth surface/mesh is NOT included — only the
+  GT camera calibration and GT sparse points. This is for SfM/pose and MVS
+  smoke testing, not full MVS-accuracy benchmarking against the scan.
+- ETH3D images are 24 MP: extraction/MVS are memory-heavy. Downscale with
+  `--SiftExtraction.max_image_size 3200` (or similar) and
+  `--PatchMatchStereo.max_image_size` to stay within RAM.
+- THIN_PRISM_FISHEYE intrinsics must be respected; don't force a pinhole model
+  on ETH3D inputs if you want the GT calibration to apply.
+- These scenes are redistributed for testing; cite/respect the original
+  sources (ETH3D: eth3d.net, CVPR 2017; COLMAP examples: colmap.github.io,
+  CVPR 2016). See README.md.

relief/dslr_calibration_jpg/cameras.txt

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+# Camera list with one line of data per camera:
+#   CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]
+# Number of cameras: 1
+0 THIN_PRISM_FISHEYE 6048 4032 3415.02 3414.94 3038.57 2007.92 0.205043 0.237111 -0.000807391 0.000294674 -0.216933 0.438642 -3.5536e-05 0.0016041

south-building/sparse/cameras.txt

@@ -0,0 +1,4 @@
+# Camera list with one line of data per camera:
+#   CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]
+# Number of cameras: 1
+1 SIMPLE_RADIAL 3072 2304 2559.68 1536 1152 -0.0204997

terrains/dslr_calibration_jpg/cameras.txt

@@ -0,0 +1,5 @@
+# Camera list with one line of data per camera:
+#   CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]
+# Number of cameras: 2
+1 THIN_PRISM_FISHEYE 6048 4032 3422.09 3419.97 3029.78 2011.92 0.210376 0.209954 0.000127743 1.51915e-05 -0.161334 0.390193 0.000604024 0.000187759
+0 THIN_PRISM_FISHEYE 6048 4032 3429.76 3429.06 3034.2 2004.51 0.213659 0.189908 -0.000960506 0.000352841 -0.119008 0.36575 0.000525215 0.00145048