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.

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

Default constructor

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

Copy constructor

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: open3d.geometry.TriangleMesh

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

image (open3d.geometry.RGBDImage) –
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.]])

Returns

open3d.geometry.PointCloud

crop(self, min_bound, max_bound)¶

Function to crop input pointcloud into output pointcloud

Parameters

min_bound (numpy.ndarray[float64[3, 1]]) – Minimum bound for point coordinate
max_bound (numpy.ndarray[float64[3, 1]]) – Maximum bound for point coordinate

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

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

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, rotation, center=True, type=RotationType.XYZ)¶

Apply rotation to the geometry coordinates and normals.

Parameters

rotation (numpy.ndarray[float64[3, 1]]) – A 3D vector that either defines the three angles for Euler rotation, or in the axis-angle representation the normalized vector defines the axis of rotation and the norm the angle around this axis.
center (bool, optional, default=True) – If true, then the rotation is applied to the centered geometry
type (open3d.geometry.RotationType, optional, default=RotationType.XYZ) – Type of rotation, i.e., an Euler format, or axis-angle.

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

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