目录

• 一、LCCP方法
• 二、代码实现
• 三、实验结果
• 四、总结
• 五、相关链接

一、LCCP方法

LCCP指的是Local Convexity-Constrained Patch，即局部凸约束补丁的意思。LCCP方法的基本思想是在图像中找到局部区域内的凸结构，并将这些结构用于分割图像或提取特征。这种方法可以帮助识别图像中的凸物体，并对它们进行分割。LCCP方法通常结合了空间和法线信息，以提高图像分割的准确性和稳定性。

LCCP算法大致可以分成两个部分：1.基于超体聚类的过分割。2.在超体聚类的基础上再聚类。
该方法流程图如下：

二、代码实现

#include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include   #include  #include  #include    using namespace std; typedef pcl::PointXYZ PointT; typedef pcl::LCCPSegmentation::SupervoxelAdjacencyList SuperVoxelAdjacencyList; //邻接线条可视化 void addSupervoxelConnectionsToViewer(pcl::PointXYZRGBA& supervoxel_center, pcl::PointCloud& adjacent_supervoxel_centers, 	std::string supervoxel_name, pcl::visualization::PCLVisualizer::Ptr& viewer) { 	vtkSmartPointer points = vtkSmartPointer::New(); 	vtkSmartPointer cells = vtkSmartPointer::New(); 	vtkSmartPointer polyLine = vtkSmartPointer::New();   	for (auto adjacent_itr = adjacent_supervoxel_centers.begin(); adjacent_itr != adjacent_supervoxel_centers.end(); ++adjacent_itr) 	{ 		points->InsertNextPoint(supervoxel_center.data); 		points->InsertNextPoint(adjacent_itr->data); 	} 	vtkSmartPointer polyData = vtkSmartPointer::New(); 	polyData->SetPoints(points); 	polyLine->GetPointIds()->SetNumberOfIds(points->GetNumberOfPoints()); 	for (unsigned int i = 0; i < points->GetNumberOfPoints(); i++) 		polyLine->GetPointIds()->SetId(i, i); 	cells->InsertNextCell(polyLine); 	polyData->SetLines(cells); 	viewer->addModelFromPolyData(polyData, supervoxel_name); }   int main(int argc, char** argv) { 	pcl::PointCloud::Ptr cloud(new pcl::PointCloud); 	pcl::PointCloud::Ptr cloud_filtered(new pcl::PointCloud); 	pcl::PCDReader reader; 	// 读入点云PCD文件 	reader.read("E:****.pcd", *cloud); 	cout << "Point cloud data: " << cloud->points.size() << " points" << endl; 	pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients); 	pcl::PointIndices::Ptr inliers(new pcl::PointIndices); 	// 创建分割对象 	pcl::SACSegmentation seg; 	// 可选择配置，设置模型系数需要优化 	seg.setOptimizeCoefficients(true); 	// 必须配置，设置分割的模型类型、所用随机参数估计方法 	seg.setModelType(pcl::SACMODEL_PLANE); 	seg.setMethodType(pcl::SAC_RANSAC); 	seg.setDistanceThreshold(0.02);// 距离阈值 单位m。距离阈值决定了点被认为是局内点时必须满足的条件 	//seg.setDistanceThreshold(0.15);// 距离阈值 单位m。距离阈值决定了点被认为是局内点时必须满足的条件 	//距离阈值表示点到估计模型的距离最大值。 	seg.setInputCloud(cloud);//输入点云 	seg.segment(*inliers, *coefficients);//实现分割，并存储分割结果到点集合inliers及存储平面模型系数coefficients 	if (inliers->indices.size() == 0) 	{ 		PCL_ERROR("Could not estimate a planar model for the given dataset."); 		return (-1); 	} 	//*********************************************************************** 	//-----------输出平面模型的系数 a,b,c,d----------- 	cout << "Model coefficients: " << coefficients->values[0] << " " 		<< coefficients->values[1] << " " 		<< coefficients->values[2] << " " 		<< coefficients->values[3] << endl; 	cout << "Model inliers: " << inliers->indices.size() << endl;  	//*********************************************************************** 	// 提取地面 	pcl::ExtractIndices extract; 	extract.setInputCloud(cloud); 	extract.setIndices(inliers); 	extract.filter(*cloud_filtered);  	cout << "Ground cloud after filtering: " << endl; 	cout << *cloud_filtered << std::endl; 	pcl::PCDWriter writer; 	writer.write("3dpoints_ground.pcd", *cloud_filtered, false);  	// 提取除地面外的物体 	extract.setNegative(true); 	extract.filter(*cloud_filtered); 	cout << "Object cloud after filtering: " << endl; 	cout << *cloud_filtered << endl; 	//writer.write(".pcd", *cloud_filtered, false);  	// 点云可视化 	boost::shared_ptrviewer0(new pcl::visualization::PCLVisualizer("显示点云")); 	//左边窗口显示输入的点云,右边的窗口显示分割后的点云 	int v1(0), v2(0); 	viewer0->createViewPort(0, 0, 0.5, 1, v1); 	viewer0->createViewPort(0.5, 0, 1, 1, v2); 	viewer0->setBackgroundColor(0, 0, 0, v1); 	viewer0->setBackgroundColor(0.3, 0.3, 0.3, v2); 	pcl::visualization::PointCloudColorHandlerCustom color_in(cloud, 255, 0, 0); 	viewer0->addPointCloud(cloud, color_in, "cloud_in", v1); 	viewer0->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "cloud_in", v1);  	viewer0->addPointCloud(cloud_filtered, "cloud_out", v2); 	viewer0->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 0, 255, 0, "cloud_out", v2); 	viewer0->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "cloud_out", v2);  	while (!viewer0->wasStopped()) 	{ 		viewer0->spinOnce(100); 		boost::this_thread::sleep(boost::posix_time::microseconds(1000)); 	}  	//*********************************************************************** 	//超体聚类  	float voxel_resolution = 0.01f;    // 设置体素大小，该设置决定底层八叉树的叶子尺寸 	float seed_resolution = 0.15f;    // 设置种子大小，该设置决定超体素的大小 	float color_importance = 0.0f;    // 设置颜色在距离测试公式中的权重，即颜色影响超体素分割结果的比重。 真实点云都是一个颜色，所以这个参数无作用 	float spatial_importance = 0.9f;  // 设置空间距离在距离测试公式中的权重，较高的值会构建非常规则的超体素，较低的值产生的体素会按照法线 	float normal_importance = 4.0f;   // 设置法向量的权重，即表面法向量影响超体素分割结果的比重。 	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 super(voxel_resolution, seed_resolution); 	super.setUseSingleCameraTransform(use_single_cam_transform); 	super.setInputCloud(cloud_filtered); //cloud_filtered 	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::Ptr> supervoxel_clusters; 	super.extract(supervoxel_clusters); 	std::multimap supervoxel_adjacency; 	super.getSupervoxelAdjacency(supervoxel_adjacency); 	pcl::PointCloud::Ptr sv_centroid_normal_cloud = pcl::SupervoxelClustering::makeSupervoxelNormalCloud(supervoxel_clusters);  	cout << "超体素分割的体素个数为：" << supervoxel_clusters.size() << endl; 	// 获取点云对应的超体素分割标签 	pcl::PointCloud::Ptr supervoxel_cloud = super.getLabeledCloud(); 	pcl::visualization::PCLVisualizer::Ptr viewer1(new pcl::visualization::PCLVisualizer("VCCS")); 	viewer1->setWindowName("超体素分割"); 	viewer1->addPointCloud(supervoxel_cloud, "超体素分割"); 	viewer1->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "超体素分割"); 	viewer1->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_OPACITY, 0.5, "超体素分割");  	//-----------------------------------------获得体素点云的邻接单元---------------------------------------------- 	multimapSupervoxelAdjacency; 	super.getSupervoxelAdjacency(SupervoxelAdjacency);  	for (auto label_itr = SupervoxelAdjacency.cbegin(); label_itr != SupervoxelAdjacency.cend();) 	{ 		uint32_t super_label = label_itr->first;//获取体素单元的标签 		pcl::Supervoxel::Ptr super_cloud = supervoxel_clusters.at(super_label);//把对应标签内的点云、体素质心、以及质心对应的法向量提取出来  		pcl::PointCloud adjacent_supervoxel_centers; 		for (auto adjacent_itr = SupervoxelAdjacency.equal_range(super_label).first; adjacent_itr != SupervoxelAdjacency.equal_range(super_label).second; ++adjacent_itr) 		{ 			pcl::Supervoxel::Ptr neighbor_supervoxel = supervoxel_clusters.at(adjacent_itr->second); 			adjacent_supervoxel_centers.push_back(neighbor_supervoxel->centroid_); 		} 		std::stringstream ss; 		ss << "supervoxel_" << super_label; 		addSupervoxelConnectionsToViewer(super_cloud->centroid_, adjacent_supervoxel_centers, ss.str(), viewer1); 		label_itr = SupervoxelAdjacency.upper_bound(super_label); 	} 	// 等待直到可视化窗口关闭 	while (!viewer1->wasStopped()) 	{ 		viewer1->spinOnce(100); 		boost::this_thread::sleep(boost::posix_time::microseconds(1000)); 	}  	//return 0;  	 //*********************************************************************** 	//LCCP分割 	float concavity_tolerance_threshold = 10; 	float smoothness_threshold = 0.8; 	uint32_t min_segment_size = 0; 	bool use_extended_convexity = false; 	bool use_sanity_criterion = false; 	pcl::LCCPSegmentation lccp; 	lccp.setConcavityToleranceThreshold(concavity_tolerance_threshold);//CC效验beta值 	lccp.setSmoothnessCheck(true, voxel_resolution, seed_resolution, smoothness_threshold); 	lccp.setKFactor(k_factor);               //CC效验的k邻点 	lccp.setInputSupervoxels(supervoxel_clusters, supervoxel_adjacency); 	lccp.setMinSegmentSize(min_segment_size);//最小分割尺寸 	lccp.segment();  	pcl::PointCloud::Ptr sv_labeled_cloud = super.getLabeledCloud(); 	pcl::PointCloud::Ptr lccp_labeled_cloud = sv_labeled_cloud->makeShared(); 	lccp.relabelCloud(*lccp_labeled_cloud); 	SuperVoxelAdjacencyList sv_adjacency_list; 	lccp.getSVAdjacencyList(sv_adjacency_list);  	pcl::visualization::PCLVisualizer::Ptr viewer2(new pcl::visualization::PCLVisualizer("LCCP超体素分割")); 	viewer2->setWindowName("LCCP超体素分割"); 	viewer2->addPointCloud(lccp_labeled_cloud, "LCCP超体素分割"); 	viewer2->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "LCCP超体素分割"); 	viewer2->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_OPACITY, 0.5, "LCCP超体素分割"); 	// 等待直到可视化窗口关闭 	while (!viewer2->wasStopped()) 	{ 		viewer2->spinOnce(100); 		boost::this_thread::sleep(boost::posix_time::microseconds(1000)); 	}  	return 0;  }   

三、实验结果

原数据

去除地面后

超体聚类过分割

LCCP分割

四、总结

从实验结果来看，LCCP算法在相似物体场景分割方面有着较好的表现，对于颜色类似但棱角分明的物体可使用该算法。

五、相关链接

[1]PCL-低层次视觉-点云分割（超体聚类）
[2]PCL_使用LCCP进行点云分割