e23-co5430-Sparse-3D-reconstruction-from-multiple-images

Structure From Motion

CO543/CO5430 Computer Vision Project, Group 13. Sparse 3D reconstruction from multiple images

Problem statement

Given a set of overlapping images of a scene, recover the camera pose for each image and a sparse 3D point cloud of the scene, using classical feature based Structure from Motion. See docs/UserGuide.md for the full pipeline explanation and datasets/templeRing/README.txt for dataset details.

Setup

python -m venv venv
source venv/bin/activate        # Windows: venv\Scripts\activate
pip install -r requirements.txt

Dataset

This repo does not commit dataset images (see .gitignore ). Download TempleRing (Middlebury Multi-View Stereo dataset) yourself and place the .png files in datasets/templeRing/images/. The calibration files (templeR_par.txt, templeR_ang.txt, README.txt) are already included.

Running

python run.py                          # ORB baseline (default), TempleRing
python run.py --feature_type sift      # SIFT variant
python run.py --out outputs/orb_run1   # custom output location

Outputs land in outputs/<feature_type>/:

• points3D.ply, points3D_open3d.ply — sparse point cloud
• cameras.json — recovered camera poses + intrinsics
• sparse/0/{cameras,images,points3D}.txt — COLMAP format (for Gaussian Splatting later)
• Console output includes the metrics your proposal commits to (keypoint counts, match counts, RANSAC inlier counts, point count, per-stage timing) plus pose accuracy against TempleRing’s ground-truth calibration

Project structure

Root/
├── run.py              # main entry point
├── sfm_baseline.py      # SfM pipeline (feature detection -> matching ->
│                          RANSAC -> incremental registration -> BA -> export)
├── evaluate.py          # ground-truth pose loading + accuracy scoring
├── config.py            # parameters
├── requirements.txt
├── datasets/templeRing/ # calibration + image folder (images gitignored)
├── outputs/             # per-run results (gitignored)
└── docs/UserGuide.md    # detailed pipeline + troubleshooting guide