Data Sources

This page describes the datasets used for training, evaluation, and testing the reconstruction pipeline.

Dataset Summary

Dataset

Source

License

Known Bias / Notes

IMC25 train

Kaggle / CVG Group

CC BY

Outdoor scenes, heritage sites; well-lit, high-resolution imagery

IMC25 test

Kaggle / CVG Group

CC BY

Includes staircase scenes and ET-type scenes; more challenging geometry

custom_warehouse

Mobile camera (internal)

Internal use only

Single lighting condition, 30 fps video frames; indoor, repetitive textures

IMC 2025 Training Dataset

The primary dataset is from the Image Matching Challenge 2025 hosted on Kaggle (provided by the Computer Vision Group).

Contents

The training split contains multi-view image collections for a variety of outdoor and heritage scenes including:

  • Ancient monuments and archaeological sites (e.g., dioscuri, cyprus, baalshamin)

  • Iconic urban landmarks (e.g., taj_mahal, sacre_coeur, trevi_fountain, piazza_san_marco, grand_place_brussels)

  • Indoor and mixed scenes (e.g., stairs, haiper series with bikes, chairs, fountains)

  • Vineyard and outdoor scenes (fbk_vineyard)

  • Scenes explicitly containing outlier images (outliers sub-scenes)

A full list of 34 dataset/scene pairs is defined in data/scenes.yaml.

Labels file

data/train_labels.csv contains ground-truth rotation matrices and translation vectors for each image, formatted as semicolon-separated values:

  • rotation_matrix — 9 floats (row-major 3×3 rotation matrix)

  • translation_vector — 3 floats (camera centre in world coordinates)

Thresholds file

data/train_thresholds.csv defines per-scene angular and translation error thresholds used to compute the mAA metric. Different scenes have different tolerance levels reflecting their physical scale.

Downloading the dataset

kaggle competitions download -c image-matching-challenge-2025
unzip image-matching-challenge-2025.zip -d data/
mv data/image-matching-challenge-2025/* data/

IMC 2025 Test Dataset

The test split is provided separately (data/test/, 75 files, ~83 MB). It includes scenes emphasising challenging conditions:

  • Stairs — repetitive geometry with few distinctive features; tests robustness of feature matching under ambiguous structure.

  • ET-type scenes — scenes from the ETs dataset with unusual viewpoints.

These scene types were chosen specifically because they expose weaknesses of standard feature matchers, requiring semi-dense matching approaches like MASt3R.

Data Versioning

All datasets are tracked with DVC:

  • data/train/ is tracked by data/train.dvc

  • data/test/ is tracked by data/test.dvc

  • data/train_labels.csv is tracked by data/train_labels.csv.dvc

  • data/train_thresholds.csv is tracked by data/train_thresholds.csv.dvc

To download the dataset:

bash kaggle competitions download -c image-matching-challenge-20
unzip image-matching-challenge-2025.zip -d data/
mv data/image-matching-challenge-2025/* data/
rm -r data/image-matching-challenge-2025

Preprocessing Assumptions

The preprocessing stage (scripts/image_processing.py) makes the following assumptions:

  • Images may contain EXIF orientation metadata; orientations are normalised before matching.

  • Blurry images (Laplacian variance below blurry_threshold in conf/preprocess.yaml) are either sharpened or excluded.

  • All images for a given scene are expected to have reasonable overlap (>20% shared field of view with at least one other image in the scene).

Known Biases and Limitations

  • The training data is heavily weighted towards outdoor heritage and landmark scenes. The model may be less accurate on indoor, industrial, or highly reflective surfaces.

  • All training images are high quality (DSLR or recent smartphone). Performance may degrade on low-resolution or heavily compressed imagery.

  • Scenes with repetitive structures (stairs, shelving, tiled floors) are systematically harder — the shortlist generator may propose incorrect pairs, and COLMAP may produce disconnected sub-models.