0
点赞
收藏
分享

微信扫一扫

Open3d-Point cloud (Open3D点云)


Point cloud

点云的基础使用教程

Visualize point cloud 点云可视化

主要方法:

import  open3d as o3d 
import numpy as np
#读取点云文件(.ply、.pcd、.xzy等格式)
pcd = o3d.io.read_point_cloud(filepath)
#可视化点云,用鼠标可以选择视图,+-(小键盘区可能不行,用主键盘区的+-)可以修改点大小
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])

从github下载点云文件,读取点云文件然后可视化:

print("Load a ply point cloud, print it, and render it")
ply_point_cloud = o3d.data.PLYPointCloud()
pcd = o3d.io.read_point_cloud(ply_point_cloud.path)
print(pcd)
print(np.asarray(pcd.points))
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])

由于github直接连不上去,这里手动复制下载地址通过代理下载(fragment.ply)。

import  open3d as o3d
import numpy as np
print("Load a ply point cloud, print it, and render it")
ply_point_cloud_path = r'fragment.ply'
#读取ply文件
pcd = o3d.io.read_point_cloud(ply_point_cloud_path)
print(pcd)
print(np.asarray(pcd.points))
#可视化ply文件
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])

Voxel downsampling 体素下采样

体素下采样使用规格体素网格进行标准下采样。通常作为点云任务的预处理。算法有两步:
1.将点放入体素
2.每个被占用的体素通过平均内部的所有点来生成一个点。

import  open3d as o3d
import numpy as np


print("Load a ply point cloud, print it, and render it")
ply_point_cloud_path = r'fragment.ply'
#读取ply文件
pcd = o3d.io.read_point_cloud(ply_point_cloud_path)
print(pcd)
print("Downsample the point cloud with a voxel of 0.05")
downpcd = pcd.voxel_down_sample(voxel_size=0.05)
print(downpcd)
o3d.visualization.draw_geometries([downpcd],
zoom=0.3412,
front=[0.4257, -0.2125, -0.8795],
lookat=[2.6172, 2.0475, 1.532],
up=[-0.0694, -0.9768, 0.2024])

Vertex normal estimation 顶点法线估计

点的法线估计。按N查看法线。-+可以控制法线显示的长度。

estimate_normals 计算每个点的法线。函数使用协方差分析查找相邻点并计算相邻点的主轴。

该函数将类​​KDTreeSearchParamHybrid​​​的实例作为参数。两个关键参数​​radius = 0.1``max_nn = 30​​,指定搜索半径和最大最近邻数。示例的搜索半径为10cm,最多只考虑30个邻居,以节省计算时间。

import  open3d as o3d
import numpy as np


print("Load a ply point cloud, print it, and render it")
ply_point_cloud_path = r'fragment.ply'
#读取ply文件
pcd = o3d.io.read_point_cloud(ply_point_cloud_path)
print(pcd)
print("Downsample the point cloud with a voxel of 0.05")
downpcd = pcd.voxel_down_sample(voxel_size=0.05)
print(downpcd)



print("Recompute the normal of the downsampled point cloud")
downpcd.estimate_normals(
search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30))

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

Access estimated vertex normal 访问估计的顶点法线

通过downpcd.normals[idx] 访问法线

print("Print a normal vector of the 0th point")
print(downpcd.normals[0])

要查看其他变量,请使用​​help(downpcd)​​​ 。法线数组可以使用 ​​np.asarray​​转换为 numpy 数组。

print("Print the normal vectors of the first 10 points")
print(np.asarray(downpcd.normals)[:10,:])

Crop point cloud 裁剪点云

​read_selection_polygon_volume​​​ 读取指定多边形区域的json文件。 ​​vol.crop_point_cloud(pcd)​​ 过滤点,只保留椅子。

import open3d as o3d
print("Load a polygon volume and use it to crop the original point cloud")
demo_crop_data_point_cloud_path = r'fragment.ply'
demo_crop_data_cropped_json_path = r'cropped.json'
pcd = o3d.io.read_point_cloud(demo_crop_data_point_cloud_path)
vol = o3d.visualization.read_selection_polygon_volume(demo_crop_data_cropped_json_path)
chair = vol.crop_point_cloud(pcd)
o3d.visualization.draw_geometries([chair],
zoom=0.7,
front=[0.5439, -0.2333, -0.8060],
lookat=[2.4615, 2.1331, 1.338],
up=[-0.1781, -0.9708, 0.1608])

Paint point cloud 点云涂色

​paint_uniform_color​​ 将所有点颜色变为统一颜色。 颜色形式是RGB , 值在[0, 1]范围 。

#Paint 涂色
print("Paint chair")
chair.paint_uniform_color([1, 0.706, 0])
o3d.visualization.draw_geometries([chair],
zoom=0.7,
front=[0.5439, -0.2333, -0.8060],
lookat=[2.4615, 2.1331, 1.338],
up=[-0.1781, -0.9708, 0.1608])

Point cloud distance 点云距离

Open3D 提供了计算从源点云到目标点云的距离的方法​​compute_point_cloud_distance​​,它为源点云中的每个点计算到目标点云中最近点的距离。

在下面的示例中,我们使用该函数来计算两个点云之间的差异。请注意,此方法还可用于计算两个点云之间的倒角(Chamfer)距离。

import  open3d as o3d
import numpy as np

# Load data
demo_crop_data_point_cloud_path = r'fragment.ply'
demo_crop_data_cropped_json_path = r'cropped.json'
pcd = o3d.io.read_point_cloud(demo_crop_data_point_cloud_path)
vol = o3d.visualization.read_selection_polygon_volume(demo_crop_data_cropped_json_path)
chair = vol.crop_point_cloud(pcd)

#计算距离,去除椅子
dists = pcd.compute_point_cloud_distance(chair)
dists = np.asarray(dists)
ind = np.where(dists > 0.01)[0]
pcd_without_chair = pcd.select_by_index(ind)
o3d.visualization.draw_geometries([pcd_without_chair],
zoom=0.3412,
front=[0.4257, -0.2125, -0.8795],
lookat=[2.6172, 2.0475, 1.532],
up=[-0.0694, -0.9768, 0.2024])

Bounding volumes 边界框

​PointCloud​​​几何类型与 Open3D 中的所有其他几何体类型一样具有边界体积块(Bounding volumes)。目前,Open3D 实现了​​AxisAlignedBoundingBox​​​和​​OrientedBoundingBox​​,也可用于裁剪几何图形。

#轴对齐边框
aabb = chair.get_axis_aligned_bounding_box()
aabb.color = (1, 0, 0)
#
obb = chair.get_oriented_bounding_box()
obb.color = (0, 1, 0)
o3d.visualization.draw_geometries([chair, aabb, obb],
zoom=0.7,
front=[0.5439, -0.2333, -0.8060],
lookat=[2.4615, 2.1331, 1.338],
up=[-0.1781, -0.9708, 0.1608])

Convex hull 凸壳

点云的凸壳是包含所有点的最小凸集。Open3D 包含计算点云的凸壳的方法​​compute_convex_hull​​​。该实现基于 ​​Qhull​​。

在下面的示例代码中,我们首先从网格中对点云进行采样,并计算作为三角形网格返回的凸壳。然后,我们将凸壳可视化为红色。

import open3d as o3d

bunny_path = r'BunnyMesh.ply'
mesh = o3d.io.read_triangle_mesh(bunny_path)
mesh.compute_vertex_normals()

pcl = mesh.sample_points_poisson_disk(number_of_points=2000)
hull, _ = pcl.compute_convex_hull()
hull_ls = o3d.geometry.LineSet.create_from_triangle_mesh(hull)
hull_ls.paint_uniform_color((1, 0, 0))
o3d.visualization.draw_geometries([pcl, hull_ls])

DBSCAN clustering DBSCAN聚类

给定点云,我们希望将局部点云聚集在一起。为此,我们可以使用聚类算法。Open3D实现了DBSCAN [​​Ester1996]​​,这是一种基于密度的聚类算法。该算法在 cluster_dbscan中实现,需要两个参数:eps定义到聚类中相邻元素的距离,min_points定义形成聚类所需的最小点数。该函数返回lebels ,其中标签-1指噪声。

import open3d as o3d
import numpy as np
from matplotlib import pyplot as plt

ply_point_cloud_path = 'fragment.ply'
pcd = o3d.io.read_point_cloud(ply_point_cloud_path)

with o3d.utility.VerbosityContextManager(
o3d.utility.VerbosityLevel.Debug) as cm:
labels = np.array(
pcd.cluster_dbscan(eps=0.02, min_points=10, print_progress=True))

max_label = labels.max()
print(f"point cloud has {max_label + 1} clusters")
colors = plt.get_cmap("tab20")(labels / (max_label if max_label > 0 else 1))
colors[labels < 0] = 0
pcd.colors = o3d.utility.Vector3dVector(colors[:, :3])
o3d.visualization.draw_geometries([pcd],
zoom=0.455,
front=[-0.4999, -0.1659, -0.8499],
lookat=[2.1813, 2.0619, 2.0999],
up=[0.1204, -0.9852, 0.1215])

此算法预计算所有点的 epsilon 半径内的所有邻居。如果所选的 epsilon 太大,则可能需要大量内存。

Plane segmentation 平面分割

Open3D 还支持使用 RANSAC 对点云中的几何基元进行分割。要查找点云中支撑最大的平面,我们可以使用​​segment_plane​​​。该方法有三个参数:​​distance_threshold​​​定义点到估计平面的最大距离才能被视为入值(inlier),​​ransac_n​​​定义随机采样点数,以及​​num_iterations​​定义随机平面采样和验证的频率。然后,该函数返回平面(a,b,c,d),以便对于平面上的每个点(x,y,z),我们都有ax+by+cz+d=0 。该函数进一步返回inlier点的索引列表。

import open3d as o3d

pcd_point_cloud_path = r'fragment.pcd'
pcd = o3d.io.read_point_cloud(pcd_point_cloud_path)

plane_model, inliers = pcd.segment_plane(distance_threshold=0.01,
ransac_n=3,
num_iterations=1000)
[a, b, c, d] = plane_model
print(f"Plane equation: {a:.2f}x + {b:.2f}y + {c:.2f}z + {d:.2f} = 0")

inlier_cloud = pcd.select_by_index(inliers)
inlier_cloud.paint_uniform_color([1.0, 0, 0])
outlier_cloud = pcd.select_by_index(inliers, invert=True)
o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud],
zoom=0.8,
front=[-0.4999, -0.1659, -0.8499],
lookat=[2.1813, 2.0619, 2.0999],
up=[0.1204, -0.9852, 0.1215])

Hidden point removal 隐藏点移除

假设您想从给定的视点渲染点云,但背景中的点泄漏到前景中,因为它们没有被其他点遮挡。为此,我们可以应用隐藏点删除算法。在 Open3D 中,[实现了 ​​Katz2007]​​ 的方法,该方法从给定视图近似点云的可见性,而无需表面重建或法向估计。

import open3d as o3d
import numpy as np

print("Convert mesh to a point cloud and estimate dimensions")
armadillo_path = r'ArmadilloMesh.ply'
mesh = o3d.io.read_triangle_mesh(armadillo_path)
mesh.compute_vertex_normals()

pcd = mesh.sample_points_poisson_disk(5000)
diameter = np.linalg.norm(
np.asarray(pcd.get_max_bound()) - np.asarray(pcd.get_min_bound()))
o3d.visualization.draw_geometries([pcd])


print("Define parameters used for hidden_point_removal")
camera = [0, 0, diameter]
radius = diameter * 100

print("Get all points that are visible from given view point")
_, pt_map = pcd.hidden_point_removal(camera, radius)

print("Visualize result")
pcd = pcd.select_by_index(pt_map)
o3d.visualization.draw_geometries([pcd])


举报

相关推荐

0 条评论