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: 7>¶

Image = <Type.Image: 8>¶

LineSet = <Type.LineSet: 4>¶

PointCloud = <Type.PointCloud: 1>¶

RGBDImage = <Type.RGBDImage: 9>¶

TetraMesh = <Type.TetraMesh: 10>¶

TriangleMesh = <Type.TriangleMesh: 6>¶

Unspecified = <Type.Unspecified: 0>¶

VoxelGrid = <Type.VoxelGrid: 2>¶

property value¶

__init__(*args, **kwargs)¶

Overloaded function.

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

Default constructor

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

Copy constructor

__init__(self: open3d.cpu.pybind.geometry.PointCloud, points: open3d.cpu.pybind.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: open3d.cpu.pybind.geometry.PointCloud, joggle_inputs: bool = False) → Tuple[open3d::geometry::TriangleMesh, List[int]]¶

Computes the convex hull of the point cloud.

Parameters: joggle_inputs (bool) – If True allows the algorithm to add random noise to the points to work around degenerate inputs. This adds the ‘QJ’ option to the qhull command.
Returns: The triangle mesh of the convex hull and the list of point indices that are part of the convex hull.
Return type: tuple(open3d.geometry.TriangleMesh, list)

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[numpy.float64[3, 1]], numpy.ndarray[numpy.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, project_valid_depth_only=True)¶

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) – The input depth image can be either a float image, or a uint16_t image.
intrinsic (open3d.camera.PinholeCameraIntrinsic) – Intrinsic parameters of the camera.
extrinsic (numpy.ndarray[numpy.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) – The depth is scaled by 1 / depth_scale.
depth_trunc (float, optional, default=1000.0) – Truncated at depth_trunc distance.
stride (int, optional, default=1) – Sampling factor to support coarse point cloud extraction.
project_valid_depth_only (bool, optional, default=True) –

Returns

open3d.geometry.PointCloud

static create_from_rgbd_image(image, intrinsic, extrinsic=(with default value), project_valid_depth_only=True)¶

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

image (open3d.geometry.RGBDImage) – The input image.
intrinsic (open3d.camera.PinholeCameraIntrinsic) – Intrinsic parameters of the camera.
extrinsic (numpy.ndarray[numpy.float64[4, 4]], optional) – array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]])
project_valid_depth_only (bool, optional, default=True) –

Returns

open3d.geometry.PointCloud

crop(*args, **kwargs)¶

Overloaded function.

crop(self, bounding_box)
Function to crop input pointcloud into output pointcloud

Parameters: bounding_box (open3d.geometry.AxisAlignedBoundingBox) – AxisAlignedBoundingBox to crop points
Returns: open3d.geometry.PointCloud

crop(self, bounding_box)
Function to crop input pointcloud into output pointcloud

Parameters: bounding_box (open3d.geometry.OrientedBoundingBox) – AxisAlignedBoundingBox to crop points
Returns: open3d.geometry.PointCloud

dimension(self)¶

Returns whether the geometry is 2D or 3D.

Returns: int

estimate_covariances(self, search_param=KDTreeSearchParamKNN with knn = 30)¶

Function to compute the covariance matrix for each point in the point cloud

Parameters: search_param (open3d.geometry.KDTreeSearchParam, optional, default=KDTreeSearchParamKNN with knn = 30) – The KDTree search parameters for neighborhood search.
Returns: None

estimate_normals(self, search_param=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=KDTreeSearchParamKNN with knn = 30) – The KDTree search parameters for neighborhood search.
fast_normal_computation (bool, optional, default=True) – If true, the normal estimation uses a non-iterative method to extract the eigenvector from the covariance matrix. This is faster, but is not as numerical stable.

Returns

None

static estimate_point_covariances(input, search_param=KDTreeSearchParamKNN with knn = 30)¶

Static function to compute the covariance matrix for each point in the given point cloud, doesn’t change the input

Parameters

input (open3d.geometry.PointCloud) – The input point cloud.
search_param (open3d.geometry.KDTreeSearchParam, optional, default=KDTreeSearchParamKNN with knn = 30) – The KDTree search parameters for neighborhood search.

Returns

open3d.utility.Matrix3dVector

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

get_min_bound(self)¶

Returns min bounds for geometry coordinates.

Returns: numpy.ndarray[numpy.float64[3, 1]]

get_oriented_bounding_box(self: open3d.cpu.pybind.geometry.Geometry3D, robust: bool = False) → open3d::geometry::OrientedBoundingBox¶

Returns the oriented bounding box for the geometry.

Computes the oriented bounding box based on the PCA of the convex hull. The returned bounding box is an approximation to the minimal bounding box.

Parameters: robust (bool) – If set to true uses a more robust method which works in degenerate cases but introduces noise to the points coordinates.
Returns: The oriented bounding box. The bounding box is oriented such that the axes are ordered with respect to the principal components.
Return type: open3d.geometry.OrientedBoundingBox

static get_rotation_matrix_from_axis_angle(rotation: numpy.ndarray[numpy.float64[3, 1]]) → numpy.ndarray[numpy.float64[3, 3]]¶

static get_rotation_matrix_from_quaternion(rotation: numpy.ndarray[numpy.float64[4, 1]]) → numpy.ndarray[numpy.float64[3, 3]]¶

static get_rotation_matrix_from_xyz(rotation: numpy.ndarray[numpy.float64[3, 1]]) → numpy.ndarray[numpy.float64[3, 3]]¶

static get_rotation_matrix_from_xzy(rotation: numpy.ndarray[numpy.float64[3, 1]]) → numpy.ndarray[numpy.float64[3, 3]]¶

static get_rotation_matrix_from_yxz(rotation: numpy.ndarray[numpy.float64[3, 1]]) → numpy.ndarray[numpy.float64[3, 3]]¶

static get_rotation_matrix_from_yzx(rotation: numpy.ndarray[numpy.float64[3, 1]]) → numpy.ndarray[numpy.float64[3, 3]]¶

static get_rotation_matrix_from_zxy(rotation: numpy.ndarray[numpy.float64[3, 1]]) → numpy.ndarray[numpy.float64[3, 3]]¶

static get_rotation_matrix_from_zyx(rotation: numpy.ndarray[numpy.float64[3, 1]]) → numpy.ndarray[numpy.float64[3, 3]]¶

has_colors(self)¶

Returns True if the point cloud contains point colors.

Returns: bool

has_covariances(self: open3d.cpu.pybind.geometry.PointCloud) → bool¶: Returns True if the point cloud contains covariances.

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. Additional information about the choice of radius for noisy point clouds can be found in Mehra et. al. ‘Visibility of Noisy Point Cloud Data’, 2010.

Parameters

camera_location (numpy.ndarray[numpy.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_consistent_tangent_plane(self, k)¶

Function to orient the normals with respect to consistent tangent planes

Parameters: k (int) – Number of k nearest neighbors used in constructing the Riemannian graph used to propogate normal orientation.
Returns: None

orient_normals_to_align_with_direction(self, orientation_reference=array([0.0, 0.0, 1.0]))¶

Function to orient the normals of a point cloud

Parameters: orientation_reference (numpy.ndarray[numpy.float64[3, 1]], optional, default=array([0., 0., 1.])) – Normals are oriented with respect to orientation_reference.
Returns: None

orient_normals_towards_camera_location(self, camera_location=array([0.0, 0.0, 0.0]))¶

Function to orient the normals of a point cloud

Parameters: camera_location (numpy.ndarray[numpy.float64[3, 1]], optional, default=array([0., 0., 0.])) – Normals are oriented with towards the camera_location.
Returns: None

paint_uniform_color(self, color)¶

Assigns each point in the PointCloud the same color.

Parameters: color (numpy.ndarray[numpy.float64[3, 1]]) – RGB color for the PointCloud.
Returns: open3d.geometry.PointCloud

random_down_sample(self, sampling_ratio)¶

Function to downsample input pointcloud into output pointcloud randomly. The sample is generated by randomly sampling the indexes from the point cloud.

Parameters: sampling_ratio (float) – Sampling ratio, the ratio of number of selected points to total number of points[0-1]
Returns: open3d.geometry.PointCloud

remove_non_finite_points(self, remove_nan=True, remove_infinite=True)¶

Function to remove non-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, print_progress=False)¶

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.
print_progress (bool, optional, default=False) – Set to True to print progress bar.

Returns

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

remove_statistical_outlier(self, nb_neighbors, std_ratio, print_progress=False)¶

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.
print_progress (bool, optional, default=False) – Set to True to print progress bar.

Returns

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

rotate(*args, **kwargs)¶

Overloaded function.

rotate(self, R)
Apply rotation to the geometry coordinates and normals.

Parameters: R (numpy.ndarray[numpy.float64[3, 3]]) – The rotation matrix
Returns: open3d.geometry.Geometry3D

rotate(self, R, center)
Apply rotation to the geometry coordinates and normals.

Parameters

R (numpy.ndarray[numpy.float64[3, 3]]) – The rotation matrix
center (numpy.ndarray[numpy.float64[3, 1]]) – Rotation center used for transformation.

Returns

open3d.geometry.Geometry3D

scale(*args, **kwargs)¶

Overloaded function.

scale(self, scale, center)
Apply scaling to the geometry coordinates.

Parameters

scale (float) – The scale parameter that is multiplied to the points/vertices of the geometry.
center (numpy.ndarray[numpy.float64[3, 1]]) – Scale center used for transformation.

Returns

open3d.geometry.Geometry3D

scale(self, scale, center)
Apply scaling to the geometry coordinates.

Parameters

scale (float) – The scale parameter that is multiplied to the points/vertices of the geometry.
center (numpy.ndarray[numpy.float64[3, 1]]) – Scale center used for transformation.

Returns

open3d.geometry.Geometry3D

segment_plane(self, distance_threshold, ransac_n, num_iterations, seed=None)¶

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.
seed (Optional[int], optional, default=None) – Seed value used in the random generator, set to None to use a random seed value with each function call.

Returns

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

select_by_index(self, indices, invert=False)¶

Function to select points from input pointcloud into output pointcloud.

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[numpy.float64[4, 4]]) –
Returns: open3d.geometry.Geometry3D

translate(self, translation, relative=True)¶

Apply translation to the geometry coordinates.

Parameters

translation (numpy.ndarray[numpy.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. Normals and colors are averaged if they exist.

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 PointCloud.VoxelDownSample. Also records point cloud index before downsampling

Parameters

voxel_size (float) – Voxel size to downsample into.
min_bound (numpy.ndarray[numpy.float64[3, 1]]) – Minimum coordinate of voxel boundaries
max_bound (numpy.ndarray[numpy.float64[3, 1]]) – Maximum coordinate of voxel boundaries
approximate_class (bool, optional, default=False) –

Returns

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

HalfEdgeTriangleMesh = <Type.HalfEdgeTriangleMesh: 7>¶

Image = <Type.Image: 8>¶

LineSet = <Type.LineSet: 4>¶

PointCloud = <Type.PointCloud: 1>¶

RGBDImage = <Type.RGBDImage: 9>¶

TetraMesh = <Type.TetraMesh: 10>¶

TriangleMesh = <Type.TriangleMesh: 6>¶

Unspecified = <Type.Unspecified: 0>¶

VoxelGrid = <Type.VoxelGrid: 2>¶

property colors¶

RGB colors of points.

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

property covariances¶

Points covariances.

Type: float64 array of shape (num_points, 3, 3), 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