MATLAB 点云密度 算法思路是首先建立kd树,然后找到每个点距离最近的点的距离,对距离求和再求平均即可。 代码如下: 1clear all;2close all;3clc;45pc = pcread('rabbit.pcd');6pc = pcdownsample(pc,'random',0.1); %降低一下数据量7pc_point = pc.Location'; %得到点云数据8kdtree = vl_kdtreebuild(...
pc_point= pc.Location'; %得到点云数据kdtree =vl_kdtreebuild(pc_point); %使用vlfeat建立kdtree dissum=0;fori=1:length(pc_point) p_cur=pc_point(:,i); [index, distance]= vl_kdtreequery(kdtree, pc_point, p_cur,'NumNeighbors',2); %寻找当前点最近的非自身点 dissum= dissum + sq...
1clear all;2close all;3clc;45pc = pcread('rabbit.pcd');6pc = pcdownsample(pc,'random',0.1); %降低一下数据量7pc_point = pc.Location'; %得到点云数据8kdtree = vl_kdtreebuild(pc_point); %使用vlfeat建立kdtree910dissum =0;11fori=1:length(pc_point)12p_cur =pc_point(:,i);13[...
kdtree = vl_kdtreebuild(X); % 计算Q的k个近邻 [index, distance] = vl_kdtreequery(kdtree, X, Q, 'NumNeighbors', 5) ; 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. KNN原理介绍: 二、邻接矩阵A的构建 1.邻接矩阵A 采用如下的公式来构造链接矩阵A,其中N (x )为x 的kNN。 需要保证邻接矩阵...
pc_point= pc.Location'; %得到点云数据kdtree =vl_kdtreebuild(pc_point); %使用vlfeat建立kdtree normE=[];fori=1:length(pc_point) p_cur=pc_point(:,i); [index, distance]= vl_kdtreequery(kdtree, pc_point, p_cur,'NumNeighbors',10); %寻找当前点最近的10个点 ...
下面是一个使用VLFeat求K近邻的一个例子 clc,clear X = rand(2,100);%一百个二维列向量 kdtree = vl_kdtreebuild(X);%构建kd树 Q = rand(2,1); [index,distance] = vl_kdtreequery(kdtree, X, Q);%返回X中与Q最近的点 [index, distance] = vl_kdtreequery(kdtree, X, Q, 'NumNeighbors'...
2. 在matlab中输入vl_version,可以得到vlfeat的版本号。 有这些东西: • TheVLFeatlibrary -SIFTexample (vl_sift) • Caltech-101running example • Visual descriptors -PHOWfeature (fast dense SIFT, vl_phow) - Vector Quantization (Elkan, vl_kmeans, vl_kdtreebuild, ...