duytranus/point-cloud-registration

Point Cloud Registration Demo

Interactive Gradio application for pairwise point cloud registration using Open3D and 3DMatch RedKitchen fragments.

Features

• —Demo Pairs: Pre-selected point cloud pairs from 3DMatch RedKitchen dataset
• —Upload Mode: Register your own point clouds
• —Multiple Algorithms:
• —RANSAC + ICP (global + local)
• —RANSAC only (global)
• —ICP only (local)
• —Hyperparameter Control: Voxel size, normal radius, FPFH radius, RANSAC iterations, ICP iterations
• —Visualization: Before/after 3D point clouds with colors
• —Metrics: Fitness, RMSE, transformation matrix
• —Download: Save aligned source point cloud

Usage

Run Locally

pip install -r requirements.txt
python app.py

Then open http://localhost:7860 in your browser.

Data Source

3DMatch Geometric Registration Benchmark - RedKitchen Scene

• —Fragment dataset: Point clouds integrated from 50 depth frames using TSDF fusion
• —Evaluation files: Ground-truth transformation matrices
• —Reference: https://3dmatch.cs.princeton.edu/

Pipeline

1. 1.Load source and target point clouds
2. 2.Preprocess: Remove non-finite points, remove duplicates, voxel downsample
3. 3.Features: Estimate normals, compute FPFH descriptors
4. 4.Global Registration: RANSAC-based feature matching
5. 5.Local Registration: ICP refinement
6. 6.Visualization: Before/after 3D views with metrics

Project Structure

├── app.py                          # Main Gradio application
├── requirements.txt                # Python dependencies
├── packages.txt                    # System dependencies
├── README.md                       # This file
│
├── scripts/
│   ├── inspect_3dmatch.py         # Inspect dataset structure
│   ├── build_pair_index.py        # Parse evaluation logs
│   ├── benchmark_pairs.py         # Benchmark pairs with Open3D
│   └── prepare_demo_pairs.py      # Prepare examples for demo
│
├── examples/
│   ├── pair_metadata.json         # Demo pair metadata
│   ├── redkitchen_pair_01_source.ply
│   ├── redkitchen_pair_01_target.ply
│   └── ...
│
└── data/
    ├── raw/3dmatch/               # (local only, not pushed)
    └── processed/                 # (local only, not pushed)

Development

Prepare Data Locally

# Download and unzip
mkdir -p data/raw/3dmatch
cd data/raw/3dmatch
wget https://3dvision.princeton.edu/projects/2016/3DMatch/downloads/fragments/7-scenes-redkitchen.zip
wget https://3dvision.princeton.edu/projects/2016/3DMatch/downloads/fragments/7-scenes-redkitchen-evaluation.zip
unzip -q 7-scenes-redkitchen.zip
unzip -q 7-scenes-redkitchen-evaluation.zip
cd ../../..

# Inspect
python scripts/inspect_3dmatch.py

# Build index
python scripts/build_pair_index.py

# Benchmark
python scripts/benchmark_pairs.py

# Prepare examples
python scripts/prepare_demo_pairs.py

Notes

• —Point clouds are stored using Git LFS (.gitattributes configured)
• —Raw dataset and processed data are not pushed to repository
• —Demo pairs are downsampled for performance (typically 30k-60k points)