虽然用pcl比较多，但是pcl运行比较慢，我很多时候还是喜欢用matlab做一个算法的验证；
项目主要是做一个物体的分割处理。
1.对物体做去噪处理
clc; clear; B=pcread('test(1).ply'); figure(1); pcshow(B); C=pcdenoise(B,'NumNeighbors',90,'Threshold',1); figure(2); pcshow(C);效果如下:(主要就是一个基于最近邻+距离的去噪，类似pcl的统计滤波)
2.做一个roi提取
roi = [-inf,+inf;-inf,inf;-10,inf]; indices = findPointsInROI(C, roi); ptCloudC = select(C,indices); figure(3); pcshow(ptCloudC); roi1 = [-inf,-8;-inf,inf;-10,inf]; indices1 = findPointsInROI(ptCloudC, roi1); ptCloudB = select(ptCloudC,indices1); figure(4); pcshow(ptCloudB);
3.想要得到上面的包，在做一个平面分割
maxDistance = 0.9; referenceVector = [0,0,-1]; maxAngularDistance = 7; [model1,inlierIndices,outlierIndices] = pcfitplane(ptCloudB,... maxDistance,referenceVector,maxAngularDistance); plane1 = select(ptCloudB,inlierIndices); remainPtCloud = select(ptCloudB,outlierIndices); figure pcshow(plane1) title('First Plane') figure pcshow(remainPtCloud) title('remainPtCloud Plane')
4.再做roi+滤波
5.聚类
minDistance = 0.9; [labels,numClusters] = pcsegdist(E,minDistance); pcshow(E.Location,labels) colormap(hsv(numClusters)) title('Point Cloud Clusters') disp(" the number of an jian:") disp(numClusters)
效果不是特别好，存在一些问题。先验证一下思路是可行的。
5.长宽高的提取
都在matlab 的文件家中。不想复制了。。。
