Open3D - 用多边形体积裁剪点云

Question

每个人，

我想从点云中裁剪并保存一个区域并保存它。

我有 BBox Coordinats (maxx,maxy,minx,miny)，它们是 Pointcloud 的 MaxP 和 MinP，我想用它制作一个多边形。 使用bbox_to_Polygon(MaxP,MinP)将 BBox 坐标转换为角点。 这些应该用于创建两个多边形。 为此，我用 pyny3D 制作了一个多面体。

现在我可以给 open3d.visualization.SelectionPolygonVolume() 提供一个 Volume，我想。 我不想使用像 Open3d Docs Crop from Cloud中描述的 JSON 文件。 所以我发现了这个How to Create a open3d.visualization.SelectionPolygonVolume Object without Loading a json Filel 。

为什么orthogonal_axis="Y" ？ 为什么不只是“Z”轴？ 在JSON 文件的示例中，Y 值为 0。我建议因为orthogonal_axis="Y" ，但我不明白为什么？ 我们不需要 PolygonVolume 吗？

我会很感激一些帮助。

我正在使用 Google Colab 和 Jupyter Notebook Python 3.6

#Vertics Poyhedrol to create a PolygonVolume
bounding_polygon = np.array([

#Vertics Polygon 1
        [488.8989868164062, 612.208984375, 286.5320129394531],
        [485.114990234375, 612.208984375, 286.5320129394531],
        [485.114990234375, 605.0880126953125, 286.5320129394531],
        [488.8989868164062, 605.0880126953125, 286.5320129394531],
#Vertics Polygon2
        [488.89898681640625, 612.208984375, 291.6619873046875], 
        [485.114990234375, 612.208984375, 291.6619873046875], 
        [485.114990234375, 605.0880126953125, 291.6619873046875],
        [488.89898681640625, 605.0880126953125, 291.6619873046875]]).astype("float64") 


vol = o3d.visualization.SelectionPolygonVolume()
vol.orthogonal_axis = "Y"
vol.axis_max = 500
vol.axis_min = 700
vol.bounding_polygon = o3d.utility.Vector3dVector(bounding_polygon)

comp = vol.crop_point_cloud(pcd)
comp

#Since I took the MaxP and MinP of the Pointcloud as BBCoords I would expect the same number of points. But I get this:
#`geometry::PointCloud with 0 points`

这是整个代码

import numpy as np
import pyny3d
import pyny3d.geoms as pyny
import open3d as o3d
from open3d import JVisualizer

path_incloud = ('/gdrive/My Drive/Colab Notebooks/Georeferenzierung/BildGeoreferenzieren/PointCloud/PointCloudFormats/Kranfundament - Cloud.ply')
pcd = o3d.io.read_point_cloud(path_incloud)
print("Input Cloud:", pcd)

visualizer = JVisualizer()
visualizer.add_geometry(pcd)
visualizer.show()

def bbox_to_Polygon(MaxP,MinP):


      p1= [MaxP[0], MaxP[1], MinP[2]]
      p2= [MaxP[0],MinP[1],MinP[2]]
      p3= [MinP[0],MaxP[1],MinP[2]]
      p4= MinP
      p5= MaxP
      p6= [MinP[0],MaxP[1],MaxP[2]]
      p7= [MinP[0],MinP[1],MaxP[2]]
      p8= [MaxP[0],MinP[1], MaxP[2]]

      listPoints1 = [p1,p3,p4,p2]
      print(listPoints1)

      listPoints2 = [p5,p6,p7,p8]
      print(listPoints2)
      return  listPoints1,listPoints2

MaxP = MaxPoint_PointCloud 
MinP = MinPoint_PointCloud

listPointsPoly1 , listPointsPoly2  = bbox_to_Polygon(MaxP= MaxP, MinP=MinP)

poly1 = pyny.Polygon(np.array(listPoints1))
poly2 = pyny.Polygon(np.array(listPoints2))

poly1.plot()
poly2.plot()

polyhedron = pyny.Polyhedron.by_two_polygons(poly1, poly2)

polyhedron.plot('b')

MaxP = MaxPoint_PointCloud 
MinP = MinPoint_PointCloud

#Vertics Poyhedrol to create a PolygonVolume
bounding_polygon = np.array([
#Vertics Polygon 1
        [488.8989868164062, 612.208984375, 286.5320129394531],
        [485.114990234375, 612.208984375, 286.5320129394531],
        [485.114990234375, 605.0880126953125, 286.5320129394531],
        [488.8989868164062, 605.0880126953125, 286.5320129394531],
#Vertics Polygon2
    [488.89898681640625, 612.208984375, 291.6619873046875], 
    [485.114990234375, 612.208984375, 291.6619873046875], 
    [485.114990234375, 605.0880126953125, 291.6619873046875],
    [488.89898681640625, 605.0880126953125, 291.6619873046875]]).astype("float64") 


vol = o3d.visualization.SelectionPolygonVolume()
vol.orthogonal_axis = "Y"
vol.axis_max = 1000
vol.axis_min = -1000
vol.bounding_polygon = o3d.utility.Vector3dVector(bounding_polygon)

comp = vol.crop_point_cloud(pcd)
print("Cropped Cloud",comp)

Answer 1

这篇文章帮助我在一个长方体的范围内裁剪了一个点云。 我也一直使用vol.crop_point_cloud(pcd)遇到geometry::PointCloud with 0 points并且无法使其正常工作，但我找到了不同的解决方案。

我最终参考了这个 PR #1218来使用 open3d.geometry.OrientedBoundingBox 长方体体积来裁剪点云。 下面的代码在 start_position 周围创建了一个 200m x 200m 的“平铺”长方体，它对应于点云中的自我车辆起始姿势，并过滤仅位于平铺内的点。

import json
import numpy as np
import open3d as o3d

CUBOID_EXTENT_METERS = 200

METERS_BELOW_START = 5
METERS_ABOVE_START = 30

def main():
  ## Point Cloud
  points = np.array([
    ## These points lie inside the cuboid
    [-2770.94365061042, 722.0595600050154, -20.004812609192445],
    [-2755.94365061042, 710.0595600050154, -20.004812609192445],
    [-2755.94365061042, 710.0595600050154, -15.004812609192445],

    ## These points lie outside the cuboid
    [-2755.94365061042 + CUBOID_EXTENT_METERS, 710.0595600050154, -15.004812609192445],
    [-2755.94365061042, 710.0595600050154 + CUBOID_EXTENT_METERS, -15.004812609192445],
  ]).reshape([-1, 3])

  point_cloud = o3d.geometry.PointCloud()
  point_cloud.points = o3d.utility.Vector3dVector(points)

  ## Start point here corresponds to an ego vehicle position start in a point cloud
  start_position = {'x': -2755.94365061042, 'y': 722.0595600050154, 'z': -20.004812609192445}
  cuboid_points = getCuboidPoints(start_position)

  points = o3d.utility.Vector3dVector(cuboid_points)
  oriented_bounding_box = o3d.geometry.OrientedBoundingBox.create_from_points(points)
  point_cloud_crop = point_cloud.crop(oriented_bounding_box)

  # View original point cloud with the cuboid, all 5 points present
  o3d.visualization.draw_geometries([point_cloud, oriented_bounding_box])

  # View cropped point cloud with the cuboid, only 3 points present
  o3d.visualization.draw_geometries([point_cloud_crop, oriented_bounding_box])

def getCuboidPoints(start_position):
  return np.array([
    # Vertices Polygon1
    [start_position['x'] + (CUBOID_EXTENT_METERS / 2), start_position['y'] + (CUBOID_EXTENT_METERS / 2), start_position['z'] + METERS_ABOVE_START], # face-topright
    [start_position['x'] - (CUBOID_EXTENT_METERS / 2), start_position['y'] + (CUBOID_EXTENT_METERS / 2), start_position['z'] + METERS_ABOVE_START], # face-topleft
    [start_position['x'] - (CUBOID_EXTENT_METERS / 2), start_position['y'] - (CUBOID_EXTENT_METERS / 2), start_position['z'] + METERS_ABOVE_START], # rear-topleft
    [start_position['x'] + (CUBOID_EXTENT_METERS / 2), start_position['y'] - (CUBOID_EXTENT_METERS / 2), start_position['z'] + METERS_ABOVE_START], # rear-topright

    # Vertices Polygon 2
    [start_position['x'] + (CUBOID_EXTENT_METERS / 2), start_position['y'] + (CUBOID_EXTENT_METERS / 2), start_position['z'] - METERS_BELOW_START],
    [start_position['x'] - (CUBOID_EXTENT_METERS / 2), start_position['y'] + (CUBOID_EXTENT_METERS / 2), start_position['z'] - METERS_BELOW_START],
    [start_position['x'] - (CUBOID_EXTENT_METERS / 2), start_position['y'] - (CUBOID_EXTENT_METERS / 2), start_position['z'] - METERS_BELOW_START],
    [start_position['x'] + (CUBOID_EXTENT_METERS / 2), start_position['y'] - (CUBOID_EXTENT_METERS / 2), start_position['z'] - METERS_BELOW_START],
  ]).astype("float64") 

if __name__ == '__main__':
  main()

Answer 2

您可以选择任何轴作为正交轴。 例如，如果您选择 Z，请使用一组 Z=0 的点来定义您的多边形。 然后设置 Z 最小值和最大值，就像使用 Z 最小值和最大值之间的多边形挤压体积一样。 希望这可以帮助。

Answer 3

这是一个缩短版本，显示如何使用np.array顶点裁剪点云：

"""
corners = [[ 5.31972845 -3.21384387  0.30217625]
 [ 5.34483288 -1.13804348  0.29917539]
 [ 7.69983939 -1.16651864  0.30329364]
 [ 7.67473496 -3.24231903  0.3062945 ]
 [ 5.31845904 -3.21276837  1.03551451]
 [ 5.34356348 -1.13696798  1.03251366]
 [ 7.69856999 -1.16544314  1.03663191]
 [ 7.67346556 -3.24124353  1.03963277]]
"""
corners = np.array(...)

# Convert the corners array to have type float64
bounding_polygon = corners.astype("float64")

# Create a SelectionPolygonVolume
vol = o3d.visualization.SelectionPolygonVolume()

# You need to specify what axis to orient the polygon to.
# I choose the "Y" axis. I made the max value the maximum Y of
# the polygon vertices and the min value the minimum Y of the
# polygon vertices.
vol.orthogonal_axis = "Y"
vol.axis_max = np.max(bounding_polygon[:, 1])
vol.axis_min = np.min(bounding_polygon[:, 1])

# Set all the Y values to 0 (they aren't needed since we specified what they
# should be using just vol.axis_max and vol.axis_min).
bounding_polygon[:, 1] = 0

# Convert the np.array to a Vector3dVector
vol.bounding_polygon = o3d.utility.Vector3dVector(bounding_polygon)

# Crop the point cloud using the Vector3dVector
cropped_pcd = vol.crop_point_cloud(pcd)

# Get a nice looking bounding box to display around the newly cropped point cloud
# (This part is optional and just for display purposes)
bounding_box = cropped_pcd.get_axis_aligned_bounding_box()
bounding_box.color = (1, 0, 0)

# Draw the newly cropped PCD and bounding box
o3d.visualization.draw_geometries([cropped_pcd, bounding_box],
                                  zoom=2,
                                  front=[5, -2, 0.5],
                                  lookat=[7.67473496, -3.24231903,  0.3062945],
                                  up=[1.0, 0.0, 0.0])

之前的例子：

之后的示例（这是来自云中心的蓝色点块）：