open3d计算网格和点云之间的距离

Question

对于一个研究项目，我尝试进行点云比较。为了保持简短，我有一个CAD文件(.stl)和几个由激光扫描仪创建的点云。现在我要计算CAD文件和每个点云之间的差异。

首先，我从Cloud Compare开始，它对获得基本的理解有很大帮助。(减少点，删除重复项，创建网格和比较距离)

在python中，我可以导入文件并进行一些基本的计算。但是，我无法计算距离。

下面是我的代码：

import numpy as np 
import open3d as o3d

#read point cloud
dataname_pcd= "pcd.xyz"
point_cloud = np.loadtxt(input_path+dataname_pcd,skiprows=1)
#read mesh 
dataname_mesh = "cad.stl"
mesh = o3d.io.read_triangle_mesh(input_path+dataname_mesh)
print (mesh)

#calulate the distance
mD = o3d.geometry.PointCloud.compute_point_cloud_distance([point_cloud],[mesh])

#calculate the distance返回此错误："TypeError: compute_point_cloud_distance()：函数参数不兼容。支持以下参数类型: 1. (self: open3d.cpu.pybind.geometry.PointCloud，target: open3d.cpu.pybind.geometry.PointCloud) -> open3d.cpu.pybind.utility.DoubleVector“

问题:需要对网格和点云进行哪些预变换来计算它们的距离？有没有推荐的方式来显示差异？

到目前为止，我只使用了下面的可视化行

o3d.visualization.draw_geometries([pcd],
                                  zoom=0.3412,
                                  front=[0.4257, -0.2125, -0.8795],
                                  lookat=[2.6172, 2.0475, 1.532],
                                  up=[-0.0694, -0.9768, 0.2024])

Answer 1

函数“计算点云距离()”需要2个点云，但其中一个几何图形是由多边形和顶点组成的网格。只需将其转换为点云即可：

pcd = o3d.geometry.PointCloud() # create a empty geometry
pcd.points = mesh.vertices      # take the vertices of your mesh

我将演示如何可视化两个云之间的距离，这两个云都是在移动的机器人( Velodyne LIDAR)上捕获的，平均间隔为1米。考虑注册前后的2个云，它们之间的距离应该会减小，对吗？下面是一些代码：

import copy
import pandas as pd
import numpy as np
import open3d as o3d
from matplotlib import pyplot as plt

# Import 2 clouds, paint and show both
pc_1 = o3d.io.read_point_cloud("scan_0.pcd") # 18,421 points
pc_2 = o3d.io.read_point_cloud("scan_1.pcd") # 19,051 points
pc_1.paint_uniform_color([0,0,1])
pc_2.paint_uniform_color([0.5,0.5,0])
o3d.visualization.draw_geometries([pc_1,pc_2])

​

​

# Calculate distances of pc_1 to pc_2. 
dist_pc1_pc2 = pc_1.compute_point_cloud_distance(pc_2)

# dist_pc1_pc2 is an Open3d object, we need to convert it to a numpy array to 
# acess the data
dist_pc1_pc2 = np.asarray(dist_pc1_pc2)

# We have 18,421 distances in dist_pc1_pc2, because cloud pc_1 has 18,421 pts.
# Let's make a boxplot, histogram and serie to visualize it.
# We'll use matplotlib + pandas. 
 
df = pd.DataFrame({"distances": dist_pc1_pc2}) # transform to a dataframe
# Some graphs
ax1 = df.boxplot(return_type="axes") # BOXPLOT
ax2 = df.plot(kind="hist", alpha=0.5, bins = 1000) # HISTOGRAM
ax3 = df.plot(kind="line") # SERIE
plt.show()

​

​

# Load a previos transformation to register pc_2 on pc_1 
# I finded it with the Fast Global Registration algorithm, in Open3D 
T = np.array([[ 0.997, -0.062 ,  0.038,  1.161],
              [ 0.062,  0.9980,  0.002,  0.031],
              [-0.038,  0.001,  0.999,  0.077],
              [ 0.0,    0.0  ,  0.0   , 1.0  ]])
# Make a copy of pc_2 to preserv the original cloud
pc_2_copy = copy.deepcopy(pc_2)
# Aply the transformation T on pc_2_copy
pc_2_copy.transform(T)
o3d.visualization.draw_geometries([pc_1,pc_2_copy]) # show again

​

​

# Calculate distances
dist_pc1_pc2_transformed = pc_1.compute_point_cloud_distance(pc_2_copy)
dist_pc1_pc2_transformed = np.asarray(dist_pc1_pc2_transformed)
# Do as before to show diferences
df_2 = pd.DataFrame({"distances": dist_pc1_pc2_transformed})
# Some graphs (after registration)
ax1 = df_2.boxplot(return_type="axes") # BOXPLOT
ax2 = df_2.plot(kind="hist", alpha=0.5, bins = 1000) # HISTOGRAM
ax3 = df_2.plot(kind="line") # SERIE
plt.show()

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