squarenet 0.1.0

Sparse local operations for point clouds in any dimension.

SquareNet

SquareNet is a lightweight framework for mapping unstructured point clouds into structured N-dimensional grids, enabling fast and efficient local operations.

✨ Key Idea

SquareNet builds a bijective mapping between point indices and a structured grid:

• Bijection: each point maps to exactly one grid cell
• Neighborhood preservation: nearby points remain close in the grid

This allows replacing expensive spatial queries (k-NN, radius search) with simple array slicing.

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🚀 Features

• Fast point cloud → grid mapping
• Invertible transformation
• Efficient local neighborhood operations
• Sparse Gram matrix computation via sliding windows
• NumPy-friendly (no heavy dependencies required)

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📦 Installation

pip install squarenet

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🧠 Basic Usage

from squarenet import SquareNet

sqnet = SquareNet(IJ=(L, W, H, ...), max_iter = 100)

# Original data: (N, D)
points = np.random.rand(N, D)
Sqnet.fit(points)

# Map (N, *) to the grid: (L, W, H, ..., *)
sqX = sqnet.map(X)

# Back to original ordering
X_rec = sqnet.invert_map(sqX)

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🔬 Local Processing Example

Compute a local (sparse) Gram matrix using a sliding window:

G = sqnet.gram(X, ws=(5, 5))

Instead of computing a full (N × N) matrix, SquareNet only computes interactions within local neighborhoods.

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🗺️ Demo Dataset

A small demo dataset (France map) is included:

Sqnet.fit("france")

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🧱 Project Structure

src/
  squarenet/
    __init__.py
    core.py
    utils.py
    data/france.geojson

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🎯 Why SquareNet?

Traditional point cloud pipelines rely on:

• k-NN search (O(N log N))
• irregular memory access
• poor GPU utilization

SquareNet enables:

• O(N) local operations
• contiguous memory access
• vectorized operations on a sliding window

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📈 Use Cases

• Point cloud processing
• Graph-to-grid transformations
• Fast kernel methods
• Local feature aggregation
• Deep learning preprocessing

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🛠️ Development

git clone https://github.com/ArmanddeCacqueray/SquareNet
cd squarenet
pip install -e .

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📄 License

MIT License

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🤝 Contributing

Contributions are welcome. Please open an issue or submit a pull request.