alexmkwizu/colmap-testing-dataset

COLMAP Testing Dataset

A small, ready-to-run collection of multi-view image scenes for testing and benchmarking the COLMAP 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):

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:

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):

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

Golden MVS reference (colmap_golden_bundle/)

The repo also includes colmap_golden_bundle/ — the CUDA patch_match_stereo golden output for the south-building scene, used to validate that a Metal (Apple-GPU) MVS port produces equivalent depth/normal maps. Generated with upstream COLMAP 4.0.4 (CUDA) on an NVIDIA T4.

colmap_golden_bundle/
├── note.md                            # full provenance + how-to (read this first)
├── colmap_cuda_golden_data.ipynb      # the idempotent notebook that produced it
├── golden_mvs/dense/
│   ├── fused.ply                       # 3,609,743 fused points (93 MB)
│   └── stereo/
│       ├── depth_maps/*.geometric.bin  # 128 geometric depth maps (the reference)
│       └── normal_maps/*.geometric.bin # 128 geometric normal maps
└── logs/                               # undistort / patch_match / fusion logs

Depth/normal maps are COLMAP dense binary: an ASCII width&height&channels& header followed by column-major (Fortran-order) float32, where 0 = invalid. To validate a port, run patch_match_stereo on south-building and diff your maps against these on overlapping valid pixels, within tolerance. A ready-to-use Python reader and the full process note live in colmap_golden_bundle/note.md.

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.