非结构化点云的区域不断增长：验证pcl失败

Question

我正在研究针对非结构化点云的python区域增长算法。我尝试使用平滑度约束根据http://pointclouds.org/documentation/tutorials/region_growing_segmentation.php#region-growing-segmentation和点云的分段实现算法。
使用相同的点云，尽管所有步骤和参数似乎都相同，但我无法通过pcl实现重现结果。我的实现产生217个区域，而pcl的区域得到24个（超分割）。点云可以找到：

https://github.com/scrox/region-growing/blob/master/test.txt

是什么原因导致这种重大差异？我也欢迎对我的python代码进行性能改进。

import math
import numpy as np
from sklearn.neighbors import KDTree
unique_rows=np.loadtxt("test.txt")
tree    = KDTree(unique_rows, leaf_size=2) 
dist,nn_glob = tree.query(unique_rows[:len(unique_rows)], k=30) 

def normalsestimation(pointcloud,nn_glob,VP=[0,0,0]):
    ViewPoint = np.array(VP)
    normals = np.empty((np.shape(pointcloud)))
    curv    = np.empty((len(pointcloud),1))
    for index in range(len(pointcloud)):
        nn_loc = pointcloud[nn_glob[index]]
        COV = np.cov(nn_loc,rowvar=False)
        eigval, eigvec = np.linalg.eig(COV)
        idx = np.argsort(eigval)
        nor = eigvec[:,idx][:,0]
        if nor.dot((ViewPoint-pointcloud[index,:])) > 0:
            normals[index] = nor
        else:
            normals[index] = - nor
        curv[index] = eigval[idx][0] / np.sum(eigval)

     return normals,curv

def regiongrowing(pointcloud,nn_glob,theta_th = 'auto', cur_th = 'auto'):
    normals,curvature = normalsestimation(pointcloud,nn_glob=nn_glob)
    order             = curvature[:,0].argsort().tolist()
    region            = []
    if theta_th == 'auto':
        theta_th          = 15.0 / 180.0 * math.pi # in radians
    if cur_th == 'auto':
        cur_th            = np.percentile(curvature,98)
    while len(order) > 0:
        region_cur = []
        seed_cur   = []
        poi_min    = order[0] #poi_order[0]
        region_cur.append(poi_min)
        seed_cur.append(poi_min)
        order.remove(poi_min)
        for i in range(len(seed_cur)):
            nn_loc  = nn_glob[seed_cur[i]]
            for j in range(len(nn_loc)):
                nn_cur = nn_loc[j]
                if all([nn_cur in order , np.arccos(np.abs(np.dot(normals[seed_cur[i]],normals[nn_cur])))<theta_th]):
                    region_cur.append(nn_cur)
                    order.remove(nn_cur)
                    if curvature[nn_cur] < cur_th:
                        seed_cur.append(nn_cur)
        region.append(region_cur)
    return region

region  = regiongrowing(unique_rows,nn_glob)

这是pcl代码：

#include <iostream>
#include <vector>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/search/search.h>
#include <pcl/search/kdtree.h>
#include <pcl/features/normal_3d.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/segmentation/region_growing.h>

int
main (int argc, char** argv)
{
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
  if ( pcl::io::loadPCDFile <pcl::PointXYZ> ("test.pcd", *cloud) == -1)
  {
    std::cout << "Cloud reading failed." << std::endl;
    return (-1);
  }

  pcl::search::Search<pcl::PointXYZ>::Ptr tree = boost::shared_ptr<pcl::search::Search<pcl::PointXYZ> > (new pcl::search::KdTree<pcl::PointXYZ>);
  pcl::PointCloud <pcl::Normal>::Ptr normals (new pcl::PointCloud <pcl::Normal>);
  pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normal_estimator;
  normal_estimator.setSearchMethod (tree);
  normal_estimator.setInputCloud (cloud);
  normal_estimator.setKSearch (30);
  normal_estimator.compute (*normals);


  pcl::RegionGrowing<pcl::PointXYZ, pcl::Normal> reg;
  reg.setSearchMethod (tree);
  reg.setNumberOfNeighbours (30);
  reg.setInputCloud (cloud);
  reg.setInputNormals (normals);
  reg.setSmoothnessThreshold (15.0 / 180.0 * M_PI);
  reg.setCurvatureThreshold (0.088614);

  std::vector <pcl::PointIndices> clusters;
  reg.extract (clusters);

  std::cout << "Number of clusters is equal to " << clusters.size () << std::endl;
  std::cout << "First cluster has " << clusters[0].indices.size () << " points." << endl;
  std::cout << "These are the indices of the points of the initial" <<                          
     std::endl << "cloud that belong to the first cluster:" << std::endl;

  int counter = 0;
  while (counter < clusters[0].indices.size ())
  {
   std::cout << clusters[0].indices[counter] << ", ";
   counter++;
   }
   std::cout << std::endl;

   pcl::PointCloud <pcl::PointXYZRGB>::Ptr colored_cloud = reg.getColoredCloud ();
   pcl::visualization::CloudViewer viewer ("Cluster viewer");
   viewer.showCloud(colored_cloud);
   while (!viewer.wasStopped ())
   {
   }

   return (0);
}

Answer 1

我认为这与您for i in range(len(seed_cur)):中的def regiongrowing(pointcloud,nn_glob,theta_th = 'auto', cur_th = 'auto'):有关。我相信当python使用for循环时，一旦循环开始，它实际上不会循环通过添加到列表中的任何元素。对于

if curvature[nn_cur] < cur_th:
      seed_cur.append(nn_cur)

它不会继续循环通过nn_cur。我使用while循环略微修改了您的代码。我的结果是21个地区。不完全像PCL中那样，而是更接近。我的功能是regiongrowing1供参考。

import math
import numpy as np
from sklearn.neighbors import KDTree


unique_rows=np.loadtxt("test.txt")
tree    = KDTree(unique_rows, leaf_size=2) 
dist,nn_glob = tree.query(unique_rows[:len(unique_rows)], k=30) 

def normalsestimation(pointcloud,nn_glob,VP=[0,0,0]):
    ViewPoint = np.array(VP)
    normals = np.empty((np.shape(pointcloud)))
    curv    = np.empty((len(pointcloud),1))
    for index in range(len(pointcloud)):
        nn_loc = pointcloud[nn_glob[index]]
        COV = np.cov(nn_loc,rowvar=False)
        eigval, eigvec = np.linalg.eig(COV)
        idx = np.argsort(eigval)
        nor = eigvec[:,idx][:,0]
        if nor.dot((ViewPoint-pointcloud[index,:])) > 0:
            normals[index] = nor
        else:
            normals[index] = - nor
        curv[index] = eigval[idx][0] / np.sum(eigval)
    return normals,curv
#seed_count = 0
#while seed_count < len(current_seeds)


def regiongrowing(pointcloud,nn_glob,theta_th = 'auto', cur_th = 'auto'):
    normals,curvature = normalsestimation(pointcloud,nn_glob=nn_glob)
    order             = curvature[:,0].argsort().tolist()
    region            = []
    if theta_th == 'auto':
        theta_th          = 15.0 / 180.0 * math.pi # in radians
    if cur_th == 'auto':
        cur_th            = np.percentile(curvature,98)
    while len(order) > 0:
        region_cur = []
        seed_cur   = []
        poi_min    = order[0] #poi_order[0]
        region_cur.append(poi_min)
        seed_cur.append(poi_min)
        order.remove(poi_min)
        for i in range(len(seed_cur)):
            nn_loc  = nn_glob[seed_cur[i]]
            for j in range(len(nn_loc)):
                nn_cur = nn_loc[j]
                if all([nn_cur in order , np.arccos(np.abs(np.dot(normals[seed_cur[i]],normals[nn_cur])))<theta_th]):
                    region_cur.append(nn_cur)
                    order.remove(nn_cur)
                    if curvature[nn_cur] < cur_th:
                        seed_cur.append(nn_cur)
        region.append(region_cur)
    return region

def regiongrowing1(pointcloud,nn_glob,theta_th = 'auto', cur_th = 'auto'):
    normals,curvature = normalsestimation(pointcloud,nn_glob=nn_glob)
    order             = curvature[:,0].argsort().tolist()
    region            = []
    if theta_th == 'auto':
        theta_th          = 15.0 / 180.0 * math.pi # in radians
    if cur_th == 'auto':
        cur_th            = np.percentile(curvature,98)
    while len(order) > 0:
        region_cur = []
        seed_cur   = []
        poi_min    = order[0] #poi_order[0]
        region_cur.append(poi_min)
        seedval = 0

        seed_cur.append(poi_min)
        order.remove(poi_min)
#        for i in range(len(seed_cur)):#change to while loop
        while seedval < len(seed_cur):
            nn_loc  = nn_glob[seed_cur[seedval]]
            for j in range(len(nn_loc)):
                nn_cur = nn_loc[j]
                if all([nn_cur in order , np.arccos(np.abs(np.dot(normals[seed_cur[seedval]],normals[nn_cur])))<theta_th]):
                    region_cur.append(nn_cur)
                    order.remove(nn_cur)
                    if curvature[nn_cur] < cur_th:
                        seed_cur.append(nn_cur)
            seedval+=1
        region.append(region_cur)
    return region

region= regiongrowing(unique_rows,nn_glob)
region1  = regiongrowing1(unique_rows,nn_glob)

尝试一下，看看会得到什么。