以下是使用KD树数据结构
查找最近邻点C的C++代码：
```c++
#include <iostream>
#include <vector>
#include <algorithm>
#include <cmath>
struct Point {
double x, y, z;
Point(double x_, double y_, double z_) : x(x_), y(y_), z(z_) {};
};
struct Node {
Point point;
Node* left;
Node* right;
Node(Point p) : point(p), left(nullptr), right(nullptr) {};
};
class KDTree {
public:
KDTree(std::vector<Point>& points) {
root = buildTree(points, 0);
}
Point findNearest(Point& target) {
nearest = root->point;
nearestDist = dist(root->point, target);
searchTree(root, target);
return nearest;
}
private:
Node* root;
Point nearest;
double nearestDist;
Node* buildTree(std::vector<Point>& points, int depth) {
if (points.empty()) {
return nullptr;
}
int axis = depth % 3;
if (points.size() == 1) {
return new Node(points[0]);
}
auto mid = points.begin() + points.size() / 2;
std::nth_element(points.begin(), mid, points.end(), [axis](const Point& a, const Point& b) {
if (axis == 0) {
return a.x < b.x;
}
else if (axis == 1) {
return a.y < b.y;
}
else {
return a.z < b.z;
}
});
auto node = new Node(*mid);
node->left = buildTree(std::vector<Point>(points.begin(), mid), depth + 1);
node->right = buildTree(std::vector<Point>(mid + 1, points.end()), depth + 1);
return node;
}
void searchTree(Node* node, Point& target) {
if (node == nullptr) {
return;
}
double d = dist(node->point, target);
if (d < nearestDist) {
nearestDist = d;
nearest = node->point;
}
int axis = depth(node) % 3;
if (axis == 0) {
if (target.x < node->point.x) {
searchTree(node->left, target);
if (target.x + nearestDist >= node->point.x) {
searchTree(node->right, target);
}
}
else {
searchTree(node->right, target);
if (target.x - nearestDist <= node->point.x) {
searchTree(node->left, target);
}
}
}
else if (axis == 1) {
if (target.y < node->point.y) {
searchTree(node->left, target);
if (target.y + nearestDist >= node->point.y) {
searchTree(node->right, target);
}
}
else {
searchTree(node->right, target);
if (target.y - nearestDist <= node->point.y) {
searchTree(node->left, target);
}
}
}
else {
if (target.z < node->point.z) {
searchTree(node->left, target);
if (target.z + nearestDist >= node->point.z) {
searchTree(node->right, target);
}
}
else {
searchTree(node->right, target);
if (target.z - nearestDist <= node->point.z) {
searchTree(node->left, target);
}
}
}
}
int depth(Node* node) {
if (node == nullptr) {
return -1;
}
return 1 + std::max(depth(node->left), depth(node->right));
}
double dist(Point& a, Point& b) {
return std::sqrt(std::pow(a.x - b.x, 2) + std::pow(a.y - b.y, 2) + std::pow(a.z - b.z, 2));
}
};
int main() {
std::vector<Point> points = {Point(1, 2, 3), Point(4, 5, 6), Point(7, 8, 9)};
KDTree tree(points);
Point target(3, 4, 5);
Point nearest = tree.findNearest(target);
std::cout << "Nearest point to target: (" << nearest.x << ", " << nearest.y << ", " << nearest.z << ")" << std::endl;
return 0;
}
```
本文介绍了如何使用MATLAB的Computer Vision Toolbox在点云数据中查找最近邻点，这一操作常见于物体识别、目标跟踪和点云配准任务。通过加载点云数据，下采样，然后利用特定函数查询指定点的最近邻点，从而简化点云处理，适用于计算机视觉和机器人等领域。
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