凹凸型分割算法适用于颜色类似、棱角分明的物体场景分割。

算法流程：

1、基于超体聚类的过分割；

2、在超体聚类的基础上再聚类。

 示例代码：

//超体聚类+LCCP
//#include "stdafx.h"

#include <stdlib.h>  
#include <cmath>  
#include <limits.h>  
#include <boost/format.hpp>  
#include <fstream> 

#include <pcl/console/parse.h>  
#include <pcl/io/pcd_io.h>  
#include <pcl/visualization/pcl_visualizer.h>  
#include <pcl/visualization/point_cloud_color_handlers.h>  
#include <pcl/visualization/cloud_viewer.h>

#include <pcl/filters/passthrough.h>  
#include <pcl/segmentation/supervoxel_clustering.h>  

#include <pcl/segmentation/lccp_segmentation.h>  

#define Random(x) (rand() % x)

typedef pcl::PointXYZRGBA PointT;
typedef pcl::LCCPSegmentation<PointT>::SupervoxelAdjacencyList SuperVoxelAdjacencyList;

int main(int argc, char ** argv)
{
	//输入点云  
	pcl::PointCloud<PointT>::Ptr input_cloud_ptr(new pcl::PointCloud<PointT>);
	pcl::PCLPointCloud2 input_pointcloud2;
	if (pcl::io::loadPCDFile("E:\\PercipioVision\\depth2pointcloud\\testdata\\test-Cloud1.pcd", input_pointcloud2))
	{
		PCL_ERROR("ERROR: Could not read input point cloud ");
		return (3);
	}
	pcl::fromPCLPointCloud2(input_pointcloud2, *input_cloud_ptr);
	PCL_INFO("Done making cloud\n");

	//粒子距离，体素大小，空间八叉树的分辨率，类kinect或xtion获取的数据，0.008左右合适
	float voxel_resolution = 2.0f;
	//晶核距离，种子的分辨率，一般可设置为体素分辨率的50倍以上
	float seed_resolution = 100.0f;
	//颜色容差，针对分割场景，如果分割场景中各个物体之间的颜色特征差异明显，可设置较大
	float color_importance = 0.1f;
	//设置较大且其他影响较小时，基本按照空间分辨率来决定体素分割
	float spatial_importance = 1.0f;
	//针对分割场景，如果分割场景中各个物体连通处的法线特征差异明显，可设置较大，
	//但在实际使用中，需要针对数据的结构适当考虑，发现估计的准确性等因素
	float normal_importance = 4.0f;
	bool use_single_cam_transform = false;
	bool use_supervoxel_refinement = false;

	unsigned int k_factor = 0;

	//voxel_resolution is the resolution (in meters) of voxels used、seed_resolution is the average size (in meters) of resulting supervoxels  
	pcl::SupervoxelClustering<PointT> super(voxel_resolution, seed_resolution);
	super.setUseSingleCameraTransform(use_single_cam_transform);
	super.setInputCloud(input_cloud_ptr);
	//Set the importance of color for supervoxels. 
	super.setColorImportance(color_importance);
	//Set the importance of spatial distance for supervoxels.
	super.setSpatialImportance(spatial_importance);
	//Set the importance of scalar normal product for supervoxels. 
	super.setNormalImportance(normal_importance);
	std::map<uint32_t, pcl::Supervoxel<PointT>::Ptr> supervoxel_clusters;

	PCL_INFO("Extracting supervoxels\n");
	super.extract(supervoxel_clusters);

	PCL_INFO("Getting supervoxel adjacency\n");
	std::multimap<uint32_t, uint32_t> supervoxel_adjacency;
	super.getSupervoxelAdjacency(supervoxel_adjacency);
	pcl::PointCloud<pcl::PointNormal>::Ptr sv_centroid_normal_cloud = pcl::SupervoxelClustering<PointT>::makeSupervoxelNormalCloud(supervoxel_clusters);

	//LCCP分割
	float concavity_tolerance_threshold = 20;
	float smoothness_threshold = 0.2;
	uint32_t min_segment_size = 0;
	bool use_extended_convexity = false;
	bool use_sanity_criterion = false;
	PCL_INFO("Starting Segmentation\n");
	pcl::LCCPSegmentation<PointT> lccp;
	//设置CC判断的依据
	lccp.setConcavityToleranceThreshold(concavity_tolerance_threshold);
	//设置是否使用阶梯检测，这个条件会检测两个超体素之间是否是一个step。
	//如果两个超体素之间的面到面距离>expected_distance + smoothness_threshold_*voxel_resolution_则这个两个超体素被判定为unsmooth并被标记为凹。
	lccp.setSmoothnessCheck(true, voxel_resolution, seed_resolution, smoothness_threshold);
	//设置CC判断中公共距离被判定为凸的个数
	lccp.setKFactor(k_factor);
	//输入超体分割后的点云
	lccp.setInputSupervoxels(supervoxel_clusters, supervoxel_adjacency);
	lccp.setMinSegmentSize(min_segment_size);
	lccp.segment();

	PCL_INFO("Interpolation voxel cloud -> input cloud and relabeling\n");

	pcl::PointCloud<pcl::PointXYZL>::Ptr sv_labeled_cloud = super.getLabeledCloud();
	pcl::PointCloud<pcl::PointXYZL>::Ptr lccp_labeled_cloud = sv_labeled_cloud->makeShared();
	lccp.relabelCloud(*lccp_labeled_cloud);
	SuperVoxelAdjacencyList sv_adjacency_list;
	lccp.getSVAdjacencyList(sv_adjacency_list);


	// 根据label值提取点云
	int j = 0;
	pcl::PointCloud<pcl::PointXYZL>::Ptr ColoredCloud2(new pcl::PointCloud<pcl::PointXYZL>);
	ColoredCloud2->height = 1;
	ColoredCloud2->width = lccp_labeled_cloud->size();
	ColoredCloud2->resize(lccp_labeled_cloud->size());
	for (int i = 0; i < lccp_labeled_cloud->size(); i++) {
		if (lccp_labeled_cloud->points[i].label == 3) {
			ColoredCloud2->points[j].x = lccp_labeled_cloud->points[i].x;
			ColoredCloud2->points[j].y = lccp_labeled_cloud->points[i].y;
			ColoredCloud2->points[j].z = lccp_labeled_cloud->points[i].z;
			ColoredCloud2->points[j].label = lccp_labeled_cloud->points[i].label;
			j++;
		}
		
	}
	pcl::io::savePCDFileASCII("E:\\PercipioVision\\depth2pointcloud\\testdata\\3.pcd", *ColoredCloud2);
	// Configure Visualizer
	//pcl::visualization::PCLVisualizer viewer = pcl::visualization::PCLVisualizer("3D Viewer", false);
	//viewer.addPointCloud(lccp_labeled_cloud, "Segmented point cloud");
	pcl::io::savePCDFileASCII("E:\\PercipioVision\\depth2pointcloud\\testdata\\分割后合并.pcd", *lccp_labeled_cloud);
	return 0;
}