malakhovks/teaching-sfm-a01260048-supplementary

Supplementary Reproducibility Package — Manuscript A01260048

This package accompanies the article “Teaching Feature-Based 2D-to-3D Reconstruction for Metaverse-Oriented Content Generation: An OpenCV Structure-from-Motion Methodology for Computer Science Students.” It provides the executable OpenCV benchmark, pinned dependencies, machine-readable results, representative scene images, and figure sources used in the study.

Contents

• benchmark_sfm.py: deterministic scene generation, feature extraction, matching, pose recovery, triangulation, quality measurement, and figure generation.
• requirements.txt: pinned Python package versions.
• media/image2.jpg: historical five-view sparse-output image used in the visual comparison.
• benchmark_output/benchmark_results.json: machine-readable results for all three scenes and all three image-count conditions.
• benchmark_output/*_views.png: representative input views.
• benchmark_output/figure1_density_comparison.png, figure2_scene_sequences.png, and figure3_accuracy_diagnostics.png: benchmark-generated source images for Figures 1–3.
• benchmark_output/figure4_c4_dynamic_bw.png: publication-resolution black-and-white C4 Dynamic diagram used as Figure 4.
• benchmark_output/figure4_c4_dynamic_bw.svg: editable vector source for Figure 4.
• benchmark_output/artifact24_points.npz: saved point/color arrays for the 24-view curved-artifact condition.

Tested environment

• Python 3.12.13
• Linux, CPU-only execution
• OpenCV 4.11.0
• NumPy 2.5.1

Reproduce the benchmark

From the archive root, create an isolated Python environment and run:

python -m pip install -r requirements.txt
python benchmark_sfm.py

The script writes results and benchmark-generated Figures 1–3 to benchmark_output/. Figure 4 is a manually authored C4 architecture diagram supplied in PNG and SVG formats. The fixed OpenCV random seed is 20260718; the remaining reconstruction parameters are declared in the script.

Expected validation range

The included JSON file is the authoritative output from the reported run. Small runtime differences across machines are expected. Camera registration, inlier ratios, reprojection RMSE values, and filtered point counts should remain consistent with the pinned dependencies and seed.

No learner or classroom observations are contained in this package.

License

benchmark_sfm.py is released under the MIT License; see LICENSE-CODE. Results, images, figures, and documentation are released under Creative Commons Attribution 4.0 International (CC BY 4.0); see LICENSE-DATA. Third-party Python dependencies retain their respective licenses.