open3d.geometry.PointCloud

class open3d.geometry.PointCloud

PointCloud class. A point cloud consists of point coordinates, and optionally point colors and point normals.

class Type

Enum class for Geometry types.

HalfEdgeTriangleMesh = Type.HalfEdgeTriangleMesh
Image = Type.Image
LineSet = Type.LineSet
PointCloud = Type.PointCloud
RGBDImage = Type.RGBDImage
TetraMesh = Type.TetraMesh
TriangleMesh = Type.TriangleMesh
Unspecified = Type.Unspecified
VoxelGrid = Type.VoxelGrid
__init__(*args, **kwargs)

Overloaded function.

  1. __init__(self: open3d.geometry.PointCloud) -> None

Default constructor

  1. __init__(self: open3d.geometry.PointCloud, arg0: open3d.geometry.PointCloud) -> None

Copy constructor

  1. __init__(self: open3d.geometry.PointCloud, points: open3d.utility.Vector3dVector) -> None

Create a PointCloud from points

clear(self)

Clear all elements in the geometry.

Returns

open3d.geometry.Geometry

cluster_dbscan(self, eps, min_points, print_progress=False)

Cluster PointCloud using the DBSCAN algorithm Ester et al., ‘A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise’, 1996. Returns a list of point labels, -1 indicates noise according to the algorithm.

Parameters

  • eps (float) – Density parameter that is used to find neighbouring points.

  • min_points (int) – Minimum number of points to form a cluster.

  • print_progress (bool, optional, default=False) – If true the progress is visualized in the console.

Returns

open3d.utility.IntVector

compute_convex_hull(self)

Computes the convex hull of the point cloud.

Returns

Tuple[open3d.geometry.TriangleMesh, List[int]]

compute_mahalanobis_distance(self)

Function to compute the Mahalanobis distance for points in a point cloud. See: https://en.wikipedia.org/wiki/Mahalanobis_distance.

Returns

open3d.utility.DoubleVector

compute_mean_and_covariance(self)

Function to compute the mean and covariance matrix of a point cloud.

Returns

Tuple[numpy.ndarray[float64[3, 1]], numpy.ndarray[float64[3, 3]]]

compute_nearest_neighbor_distance(self)

Function to compute the distance from a point to its nearest neighbor in the point cloud

Returns

open3d.utility.DoubleVector

compute_point_cloud_distance(self, target)

For each point in the source point cloud, compute the distance to the target point cloud.

Parameters

target (open3d.geometry.PointCloud) – The target point cloud.

Returns

open3d.utility.DoubleVector

static create_from_depth_image(depth, intrinsic, extrinsic=(with default value), depth_scale=1000.0, depth_trunc=1000.0, stride=1)
Factory function to create a pointcloud from a depth image and a

camera. Given depth value d at (u, v) image coordinate, the corresponding 3d point is:

  • z = d / depth_scale

  • x = (u - cx) * z / fx

  • y = (v - cy) * z / fy

Parameters

  • depth (open3d.geometry.Image) –

  • intrinsic (open3d.camera.PinholeCameraIntrinsic) –

  • extrinsic (numpy.ndarray[float64[4, 4]], optional) – array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]])

  • depth_scale (float, optional, default=1000.0) –

  • depth_trunc (float, optional, default=1000.0) –

  • stride (int, optional, default=1) –

Returns

open3d.geometry.PointCloud

static create_from_rgbd_image(image, intrinsic, extrinsic=(with default value))
Factory function to create a pointcloud from an RGB-D image and a

camera. Given depth value d at (u, v) image coordinate, the corresponding 3d point is:

  • z = d / depth_scale

  • x = (u - cx) * z / fx

  • y = (v - cy) * z / fy

Parameters
Returns

open3d.geometry.PointCloud

crop(bounding_box, bounding_box)

Function to crop input pointcloud into output pointcloud

Parameters
Returns

open3d.geometry.PointCloud

dimension(self)

Returns whether the geometry is 2D or 3D.

Returns

int

estimate_normals(self, search_param=geometry::KDTreeSearchParamKNN with knn = 30, fast_normal_computation=True)

Function to compute the normals of a point cloud. Normals are oriented with respect to the input point cloud if normals exist

Parameters

  • search_param (open3d.geometry.KDTreeSearchParam, optional, default=geometry::KDTreeSearchParamKNN with knn = 30) – The KDTree search parameters for neighborhood search.

  • fast_normal_computation (bool, optional, default=True) – If true, the normal estiamtion uses a non-iterative method to extract the eigenvector from the covariance matrix. This is faster, but is not as numerical stable.

Returns

bool

get_axis_aligned_bounding_box(self)

Returns an axis-aligned bounding box of the geometry.

Returns

open3d.geometry.AxisAlignedBoundingBox

get_center(self)

Returns the center of the geometry coordinates.

Returns

numpy.ndarray[float64[3, 1]]

get_geometry_type(self)

Returns one of registered geometry types.

Returns

open3d.geometry.Geometry.GeometryType

get_max_bound(self)

Returns max bounds for geometry coordinates.

Returns

numpy.ndarray[float64[3, 1]]

get_min_bound(self)

Returns min bounds for geometry coordinates.

Returns

numpy.ndarray[float64[3, 1]]

get_oriented_bounding_box(self)

Returns an oriented bounding box of the geometry.

Returns

open3d.geometry.OrientedBoundingBox

static get_rotation_matrix_from_axis_angle(rotation: numpy.ndarray[float64[3, 1]]) → numpy.ndarray[float64[3, 3]]
static get_rotation_matrix_from_quaternion(rotation: numpy.ndarray[float64[4, 1]]) → numpy.ndarray[float64[3, 3]]
static get_rotation_matrix_from_xyz(rotation: numpy.ndarray[float64[3, 1]]) → numpy.ndarray[float64[3, 3]]
static get_rotation_matrix_from_xzy(rotation: numpy.ndarray[float64[3, 1]]) → numpy.ndarray[float64[3, 3]]
static get_rotation_matrix_from_yxz(rotation: numpy.ndarray[float64[3, 1]]) → numpy.ndarray[float64[3, 3]]
static get_rotation_matrix_from_yzx(rotation: numpy.ndarray[float64[3, 1]]) → numpy.ndarray[float64[3, 3]]
static get_rotation_matrix_from_zxy(rotation: numpy.ndarray[float64[3, 1]]) → numpy.ndarray[float64[3, 3]]
static get_rotation_matrix_from_zyx(rotation: numpy.ndarray[float64[3, 1]]) → numpy.ndarray[float64[3, 3]]
has_colors(self)

Returns True if the point cloud contains point colors.

Returns

bool

has_normals(self)

Returns True if the point cloud contains point normals.

Returns

bool

has_points(self)

Returns True if the point cloud contains points.

Returns

bool

hidden_point_removal(self, camera_location, radius)

Removes hidden points from a point cloud and returns a mesh of the remaining points. Based on Katz et al. ‘Direct Visibility of Point Sets’, 2007.

Parameters

  • camera_location (numpy.ndarray[float64[3, 1]]) – All points not visible from that location will be reomved

  • radius (float) – The radius of the sperical projection

Returns

Tuple[open3d.geometry.TriangleMesh, List[int]]

is_empty(self)

Returns True iff the geometry is empty.

Returns

bool

normalize_normals(self)

Normalize point normals to length 1.

Returns

open3d.geometry.PointCloud

orient_normals_to_align_with_direction(self, orientation_reference=array([0., 0., 1.]))

Function to orient the normals of a point cloud

Parameters

orientation_reference (numpy.ndarray[float64[3, 1]], optional, default=array([0., 0., 1.])) – Normals are oriented with respect to orientation_reference.

Returns

bool

orient_normals_towards_camera_location(self, camera_location=array([0., 0., 0.]))

Function to orient the normals of a point cloud

Parameters

camera_location (numpy.ndarray[float64[3, 1]], optional, default=array([0., 0., 0.])) – Normals are oriented with towards the camera_location.

Returns

bool

paint_uniform_color(self, color)

Assigns each point in the PointCloud the same color.

Parameters

color (numpy.ndarray[float64[3, 1]]) – RGB color for the PointCloud.

Returns

open3d.geometry.PointCloud

remove_none_finite_points(self, remove_nan=True, remove_infinite=True)

Function to remove none-finite points from the PointCloud

Parameters

  • remove_nan (bool, optional, default=True) – Remove NaN values from the PointCloud

  • remove_infinite (bool, optional, default=True) – Remove infinite values from the PointCloud

Returns

open3d.geometry.PointCloud

remove_radius_outlier(self, nb_points, radius)

Function to remove points that have less than nb_points in a given sphere of a given radius

Parameters

  • nb_points (int) – Number of points within the radius.

  • radius (float) – Radius of the sphere.

Returns

Tuple[open3d.geometry.PointCloud, List[int]]

remove_statistical_outlier(self, nb_neighbors, std_ratio)

Function to remove points that are further away from their neighbors in average

Parameters

  • nb_neighbors (int) – Number of neighbors around the target point.

  • std_ratio (float) – Standard deviation ratio.

Returns

Tuple[open3d.geometry.PointCloud, List[int]]

rotate(self, R, center=True)

Apply rotation to the geometry coordinates and normals.

Parameters

  • R (numpy.ndarray[float64[3, 3]]) – The rotation matrix

  • center (bool, optional, default=True) – If true, then the rotation is applied to the centered geometry

Returns

open3d.geometry.Geometry3D

scale(self, scale, center=True)

Apply scaling to the geometry coordinates.

Parameters

  • scale (float) – The scale parameter that is multiplied to the points/vertices of the geometry

  • center (bool, optional, default=True) – If true, then the scale is applied to the centered geometry

Returns

open3d.geometry.Geometry3D

segment_plane(self, distance_threshold, ransac_n, num_iterations)

Segments a plane in the point cloud using the RANSAC algorithm.

Parameters

  • distance_threshold (float) – Max distance a point can be from the plane model, and still be considered an inlier.

  • ransac_n (int) – Number of initial points to be considered inliers in each iteration.

  • num_iterations (int) – Number of iterations.

Returns

Tuple[numpy.ndarray[float64[4, 1]], List[int]]

select_down_sample(self, indices, invert=False)

Function to select points from input pointcloud into output pointcloud. indices: Indices of points to be selected. invert: Set to True to invert the selection of indices.

Parameters

  • indices (List[int]) – Indices of points to be selected.

  • invert (bool, optional, default=False) – Set to True to invert the selection of indices.

Returns

open3d.geometry.PointCloud

transform(self, arg0)

Apply transformation (4x4 matrix) to the geometry coordinates.

Parameters

arg0 (numpy.ndarray[float64[4, 4]]) –

Returns

open3d.geometry.Geometry3D

translate(self, translation, relative=True)

Apply translation to the geometry coordinates.

Parameters

  • translation (numpy.ndarray[float64[3, 1]]) – A 3D vector to transform the geometry

  • relative (bool, optional, default=True) – If true, the translation vector is directly added to the geometry coordinates. Otherwise, the center is moved to the translation vector.

Returns

open3d.geometry.Geometry3D

uniform_down_sample(self, every_k_points)

Function to downsample input pointcloud into output pointcloud uniformly. The sample is performed in the order of the points with the 0-th point always chosen, not at random.

Parameters

every_k_points (int) – Sample rate, the selected point indices are [0, k, 2k, …]

Returns

open3d.geometry.PointCloud

voxel_down_sample(self, voxel_size)

Function to downsample input pointcloud into output pointcloud with a voxel

Parameters

voxel_size (float) – Voxel size to downsample into.

Returns

open3d.geometry.PointCloud

voxel_down_sample_and_trace(self, voxel_size, min_bound, max_bound, approximate_class=False)

Function to downsample using geometry.PointCloud.VoxelDownSample also records point cloud index before downsampling

Parameters

  • voxel_size (float) – Voxel size to downsample into.

  • min_bound (numpy.ndarray[float64[3, 1]]) – Minimum coordinate of voxel boundaries

  • max_bound (numpy.ndarray[float64[3, 1]]) – Maximum coordinate of voxel boundaries

  • approximate_class (bool, optional, default=False) –

Returns

Tuple[open3d.geometry.PointCloud, numpy.ndarray[int32[m, n]]]

HalfEdgeTriangleMesh = Type.HalfEdgeTriangleMesh
Image = Type.Image
LineSet = Type.LineSet
PointCloud = Type.PointCloud
RGBDImage = Type.RGBDImage
TetraMesh = Type.TetraMesh
TriangleMesh = Type.TriangleMesh
Unspecified = Type.Unspecified
VoxelGrid = Type.VoxelGrid
property colors

RGB colors of points.

Type

float64 array of shape (num_points, 3), range [0, 1] , use numpy.asarray() to access data

property normals

Points normals.

Type

float64 array of shape (num_points, 3), use numpy.asarray() to access data

property points

Points coordinates.

Type

float64 array of shape (num_points, 3), use numpy.asarray() to access data