Building a Forest of K-D Trees with Nanoflann in C++
Nanoflann is a powerful C++ library designed for fast nearest neighbor searches using k-d trees. While using a single k-d tree is effective for many applications, sometimes you need a more robust solution. Enter the "forest" of k-d trees: a collection of trees that can handle data with complex distributions or high dimensionality more efficiently. This article explores how to create a vector of Nanoflann's KDTrees in C++ for enhanced search capabilities.
The Scenario: Need for a Forest
Imagine you're building a 3D model recognition system. Your dataset consists of thousands of 3D point clouds, each representing a different object. Searching for the nearest neighbor (most similar object) based on a new point cloud is crucial. A single k-d tree might struggle to handle this diverse dataset effectively. A vector of k-d trees, each representing a subset of the data, allows for parallel searching and improved accuracy.
The Code: Creating the Forest
#include <nanoflann.hpp>
#include <vector>
// Define your data structure
struct PointCloud {
float x, y, z;
};
// Define a custom adapter for Nanoflann
template<typename T>
struct PointCloudAdaptor {
const std::vector<T> &data;
PointCloudAdaptor(const std::vector<T> &data) : data(data) {}
// Accessors for Nanoflann's k-d tree construction
inline size_t kdtree_get_point_count() const { return data.size(); }
inline float kdtree_get_pt(size_t idx, size_t dim) const {
if (dim == 0) return data[idx].x;
if (dim == 1) return data[idx].y;
if (dim == 2) return data[idx].z;
return 0; // Error handling for invalid dimensions
}
// Optional: Distance function (Euclidean distance)
inline float kdtree_distance(const T& p1, const T& p2) const {
return sqrt(pow(p1.x - p2.x, 2) + pow(p1.y - p2.y, 2) + pow(p1.z - p2.z, 2));
}
};
int main() {
// Sample data - create a vector of point clouds
std::vector<std::vector<PointCloud>> pointClouds;
// Fill pointClouds with your actual data
// Define the number of k-d trees
int numTrees = 4; // You can change this value based on your data
// Create a vector to store the k-d trees
std::vector<nanoflann::KDTreeSingleIndex<PointCloudAdaptor<PointCloud>, 3, nanoflann::L2_Simple_Adaptor<float, PointCloudAdaptor<PointCloud> > > > trees;
// Create each k-d tree in the vector
for (int i = 0; i < numTrees; ++i) {
// Assuming you divide your data into equal subsets
auto startIndex = i * pointClouds.size() / numTrees;
auto endIndex = (i + 1) * pointClouds.size() / numTrees;
std::vector<PointCloud> subset(pointClouds.begin() + startIndex, pointClouds.begin() + endIndex);
// Create a new k-d tree for the subset
PointCloudAdaptor<PointCloud> adaptor(subset);
trees.emplace_back(3, adaptor, nanoflann::KDTreeSingleIndexAdaptorParams(10 /* max leaf */));
// Build the tree
trees.back().buildIndex();
}
// Now you have a vector of k-d trees, ready for nearest neighbor searches!
// Example usage - search for a nearest neighbor on the first tree
PointCloud queryPoint = {1.0f, 2.0f, 3.0f};
size_t retIndex;
float outDistSq;
nanoflann::KNNResultSet<float> resultSet(1);
resultSet.init(&retIndex, &outDistSq);
trees[0].findNeighbors(resultSet, &queryPoint, nanoflann::SearchParams(10));
// Use retIndex and outDistSq for the nearest neighbor information
return 0;
}
Breaking Down the Forest
The code above demonstrates the essential steps to create a vector of Nanoflann's KDTrees:
- Data Structure: Define the structure of your data points (
PointCloudin our example). - Adapter: Create a custom adapter class that provides access to your data structure for Nanoflann's k-d tree construction.
- Vector of Trees: Declare a
std::vectorto hold your k-d trees. - Tree Creation: Iterate through your data subsets and create a new
nanoflann::KDTreeSingleIndexobject for each subset. - Building: Use
buildIndex()to construct the k-d tree for each subset.
The Benefits of a Forest
Using a vector of k-d trees offers numerous advantages:
- Parallel Search: You can perform nearest neighbor searches on multiple trees simultaneously, speeding up the process.
- Balanced Data Distribution: By dividing your data into subsets, you ensure that each k-d tree is responsible for a smaller, more manageable portion of the data.
- Handling Complex Data: A forest of k-d trees is particularly useful for datasets with high dimensionality or complex distributions, where a single tree might struggle to find accurate nearest neighbors.
Conclusion
Creating a vector of Nanoflann's KDTrees empowers you to handle large and complex datasets with greater efficiency and accuracy. This technique is especially valuable for tasks that involve nearest neighbor searches within high-dimensional spaces, such as 3D model recognition, machine learning, or image processing. Remember to adapt the code to your specific data structure and application needs.
Additional Resources:
- Nanoflann Documentation: https://www.nanoflann.org/
- Nanoflann GitHub Repository: https://github.com/jlblancoc/nanoflann
- K-D Tree Tutorial: https://en.wikipedia.org/wiki/K-d_tree
