Trait PointCloudNeighbors 

pub trait PointCloudNeighbors {
    // Required methods
    fn k_nearest_neighbors(&self, k: usize) -> Vec<Vec<(usize, f32)>>;
    fn find_k_nearest(&self, query: &Point3f, k: usize) -> Vec<(usize, f32)>;
    fn find_radius_neighbors(
        &self,
        query: &Point3f,
        radius: f32,
    ) -> Vec<(usize, f32)>;
    fn find_k_nearest_brute_force(
        &self,
        query: &Point3f,
        k: usize,
    ) -> Vec<(usize, f32)>;
    fn find_radius_neighbors_brute_force(
        &self,
        query: &Point3f,
        radius: f32,
    ) -> Vec<(usize, f32)>;
}

Extension trait for PointCloud to add k-nearest neighbors functionality

Required Methods

fn k_nearest_neighbors(&self, k: usize) -> Vec<Vec<(usize, f32)>>

Find k nearest neighbors for each point in the cloud using KD-tree

This method returns a vector where each element contains the indices and distances of the k nearest neighbors for the corresponding point in the cloud.

Arguments

• k - Number of nearest neighbors to find for each point

Returns

• Vec<Vec<(usize, f32)>> - Vector of neighbor results for each point

Example

// Temporarily disabled due to stack overflow - needs investigation
use threecrate_core::{PointCloud, Point3f};
use threecrate_algorithms::point_cloud_ops::PointCloudNeighbors;

let mut cloud = PointCloud::new();
cloud.push(Point3f::new(0.0, 0.0, 0.0));
cloud.push(Point3f::new(1.0, 0.0, 0.0));
cloud.push(Point3f::new(0.0, 1.0, 0.0));

let neighbors = cloud.k_nearest_neighbors(2);
// neighbors[0] contains the 2 nearest neighbors for point 0

fn find_k_nearest(&self, query: &Point3f, k: usize) -> Vec<(usize, f32)>

Find k nearest neighbors for a specific query point using KD-tree

Arguments

• query - The query point to find neighbors for
• k - Number of nearest neighbors to find

Returns

• Vec<(usize, f32)> - Vector of (index, distance) pairs for the k nearest neighbors

fn find_radius_neighbors( &self, query: &Point3f, radius: f32, ) -> Vec<(usize, f32)>

Find all neighbors within a given radius using KD-tree

Arguments

• query - The query point to find neighbors for
• radius - Search radius

Returns

• Vec<(usize, f32)> - Vector of (index, distance) pairs for neighbors within radius

fn find_k_nearest_brute_force( &self, query: &Point3f, k: usize, ) -> Vec<(usize, f32)>

Find k nearest neighbors using brute force search (for small datasets or testing)

This method is useful for small point clouds or when you want to verify the results of the KD-tree implementation.

Arguments

• query - The query point to find neighbors for
• k - Number of nearest neighbors to find

Returns

• Vec<(usize, f32)> - Vector of (index, distance) pairs for the k nearest neighbors

fn find_radius_neighbors_brute_force( &self, query: &Point3f, radius: f32, ) -> Vec<(usize, f32)>

Find all neighbors within a given radius using brute force search

Arguments

• query - The query point to find neighbors for
• radius - Search radius

Returns

• Vec<(usize, f32)> - Vector of (index, distance) pairs for neighbors within radius

Dyn Compatibility

This trait is dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety".

Implementations on Foreign Types

impl PointCloudNeighbors for PointCloud<Point3f>

fn k_nearest_neighbors(&self, k: usize) -> Vec<Vec<(usize, f32)>>

fn find_k_nearest(&self, query: &Point3f, k: usize) -> Vec<(usize, f32)>

fn find_radius_neighbors( &self, query: &Point3f, radius: f32, ) -> Vec<(usize, f32)>

fn find_k_nearest_brute_force( &self, query: &Point3f, k: usize, ) -> Vec<(usize, f32)>

fn find_radius_neighbors_brute_force( &self, query: &Point3f, radius: f32, ) -> Vec<(usize, f32)>

Implementors