scikdtree 0.0.0

This project provides a lazily built KD_TREE operating on particles or triangle faces in three dimensional space.

KD-Tree

A high-performance, lazily-built KD-Tree implementation for spatial partitioning of particles and triangle meshes in three-dimensional space. This library provides efficient ray-triangle intersection queries and is particularly suited for polyhedral geometry operations.

Features

• Lazy Construction: KD-Tree nodes are built on-demand, optimizing memory usage and initialization time
• Multiple Split Algorithms: Choose from several plane selection strategies:
  • LOG - O(n log n) complexity (default, recommended)
  • LOGSQUARED - O(n log² n) complexity
  • QUADRATIC - O(n²) complexity
  • NOTREE - No tree structure (brute force)
• Parallel Execution: Support for multiple parallelization backends:
  • OpenMP (OMP)
  • Intel Threading Building Blocks (TBB)
• Thread-Safe: Safe for concurrent ray intersection queries
• Python Bindings: Full Python interface via nanobind
• Flexible Input: Support for vertices/faces arrays, particle clouds, or Tetgen .node/.face files
• Configurable Logging: Multiple logging levels from TRACE to OFF

Requirements

C++ Library

• CMake >= 3.16
• C++20 compatible compiler
• Dependencies (automatically fetched):
  • spdlog (logging)
  • Thrust (parallel algorithms)
  • TBB/OpenMP (optional, for parallelization)
  • Google Test (for testing)

Python Interface

• Python >= 3.9
• Dependencies (automatically fetched):
  • scikit-build-core >= 0.4.3
  • nanobind >= 1.3.2

Installation

Building the C++ Library

cd <repo-root>

# Create build directory
mkdir build && cd build

# Configure with CMake
cmake ..

# Build
cmake --build .

Installing the Python Package

# Using pip
pip install .

# Or with conda environment
conda env create -f environment.yml
conda activate kdtree-env
pip install .

Usage

C++ Example

#include "KDTree/tree/KDTree.h"
#include "KDTree/plane_selection/PlaneSelectionAlgorithm.h"

using namespace kdtree;

// Option 1: From vertices and faces
std::vector<Vertex> vertices = {{0, 0, 0}, {1, 0, 0}, {0, 1, 0}, {0, 0, 1}};
std::vector<IndexVector> faces = {{0, 1, 2}, {0, 1, 3}, {0, 2, 3}, {1, 2, 3}};
KDTree tree(vertices, faces, PlaneSelectionAlgorithm::Algorithm::LOG);

// Option 2: From .node and .face files
KDTree tree("path/to/model.node", "path/to/model.face", 
            PlaneSelectionAlgorithm::Algorithm::LOG);

// Option 3: From particles
std::vector<Vertex> particles = {{0, 0, 0}, {1, 1, 1}, {2, 2, 2}};
KDTree tree(particles, PlaneSelectionAlgorithm::Algorithm::LOG);

// Prebuild the entire tree (optional, bypasses lazy loading)
tree.prebuildTree();

// Perform ray intersection queries
Vertex origin = {0, 0, 0};
Vertex ray = {1, 0, 0};

// Count intersections
size_t count = tree.countIntersections(origin, ray);

// Get intersection points
std::set<Vertex> intersections;
tree.getIntersections(origin, ray, intersections);

Python Example

from kdtree_py import KDTree, PlaneSelectionAlgorithm
import numpy as np

# Create vertices and faces
vertices = [[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]]
faces = [[0, 1, 2], [0, 1, 3], [0, 2, 3], [1, 2, 3]]

# Build KD-Tree
tree = KDTree(vertices, faces, PlaneSelectionAlgorithm.LOG)

# Prebuild tree (optional)
tree.prebuildTree()

# Ray intersection query
origin = [0, 0, 0]
ray = [1, 0, 0]

# Count intersections
count = tree.countIntersections(origin, ray)
print(f"Number of intersections: {count}")

# Get intersection points
intersections = tree.getIntersections(origin, ray)
print(f"Intersection points: {intersections}")

# Print tree structure
tree.printTree()

CMake

For advanced CMake configuration and the full preset reference, refer to the documentation.

Algorithm Comparison

Algorithm	Time Complexity	Best Use Case
LOG	O(n log n)	Recommended for most cases
LOGSQUARED	O(n log² n)	Unoptimized LOG variant, useful for validation
QUADRATIC	O(n²)	Small datasets only
NOTREE	O(n) build, O(n) query	Baseline comparison

Project Structure

kd-tree/
├── src/
│   ├── KDTree/              # Core C++ library
│   │   ├── tree/            # KD-Tree implementation
│   │   ├── model/           # Geometry models
│   │   ├── plane_selection/ # Split plane algorithms
│   │   ├── input/           # File I/O (Tetgen support)
│   │   └── util/            # Utilities
│   ├── KDTreePython/        # Python bindings
│   ├── kd_tree_main.cpp     # C++ example application
│   └── kd_time_main.cpp     # Benchmark application
├── test/                    # Unit tests
├── docs/                    # Documentation
├── resources/               # Example mesh files
└── cmake/                   # CMake modules

Performance Tips

1. Use the LOG algorithm: Best balance of build time and query performance
2. Prebuild for multiple queries: Call prebuildTree() if you'll perform many intersection tests
3. Enable parallelization: Use TBB or OpenMP for large meshes
4. Reduce logging in production: Set KD_TREE_LOGGING_LEVEL to ERROR or OFF for maximum performance

Testing and Benchmarking

Please refer to the documentation for instructions on running unit tests and benchmarks.

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

Author

Ben Frauenknecht ([e-mail])

Acknowledgments

CMake configuration adapted from the ESA Polyhedral Gravity Model project.

Original authors: Schuhmacher, J., Blazquez, E., Gratl, F., Izzo, D., & Gómez, P.

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

Citation

If you use this library in your research, please cite it appropriately.