PointOctree

A high-performance C++ library inspired by the LidarPointCloud plugin in Unreal Engine. This library provides efficient spatial partitioning, raycasting, and geometric operations using an octree structure, tailored for handling 3D point clouds and spatial queries.

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Features

• Octree Data Structure: Optimized for managing and querying large 3D point clouds.
• Raycasting and Intersection: Supports ray-box, ray-sphere, and ray-node intersection tests.
• Spatial Queries: Retrieve points or indices within a bounding sphere or box.
• Morton Codes: Efficient spatial hashing for organizing points in the octree.
• Batch Processing: Perform operations like sphere casting on multiple rays simultaneously.
• Multithreading Support: Leverages OpenMP for parallel processing of large datasets.

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Usage

Creating an Octree

#include "pointOctree.h"

int main() {

    std::vector<std::vector<double>> points = {
        {0.0, 0.0, 0.0},
        {1.0, 1.0, 1.0},
        {2.0, 2.0, 2.0}
    };

    // Initialize the octree with a maximum depth of 16
    pointOctree octree(16);
    octree.create(points);

    // Query points within a sphere
    std::vector<size_t> indices;
    octree.getPointIndicesIntersectingSphere({1.0, 1.0, 1.0}, 1.5, indices);

    for (size_t idx : indices) {
        std::cout << "Point index: " << idx << std::endl;
    }

    return 0;
}

Sphere Cast

traces a sphere of given radius along the direction of ray. populates HitInfo with trace result.

pLine c_ray;
c_ray.p0 = {raystart.x, raystart.y, raystart.z};
c_ray.dir = {ray.direction.x, ray.direction.y, ray.direction.z};

octree.sphereCast(c_ray, 0.01, 20, hitinfo);

// do a batch sphere cast with parallelization support using OpenMP
octree.batchSphereCast(rays, 0.01, 20, hitinfos);