open3d.t.geometry.PointCloud¶

class open3d.t.geometry.PointCloud¶

A point cloud contains a list of 3D points. The point cloud class stores the attribute data in key-value maps, where the key is a string representing the attribute name and the value is a Tensor containing the attribute data.

The attributes of the point cloud have different levels:

import open3d as o3d

device = o3d.core.Device("CPU:0")
dtype = o3d.core.float32

# Create an empty point cloud
# Use pcd.point to access the points' attributes
pcd = o3d.t.geometry.PointCloud(device)

# Default attribute: "positions".
# This attribute is created by default and is required by all point clouds.
# The shape must be (N, 3). The device of "positions" determines the device
# of the point cloud.
pcd.point["positions"] = o3d.core.Tensor([[0, 0, 0],
                                          [1, 1, 1],
                                          [2, 2, 2]], dtype, device)

# Common attributes: "normals", "colors".
# Common attributes are used in built-in point cloud operations. The
# spellings must be correct. For example, if "normal" is used instead of
# "normals", some internal operations that expects "normals" will not work.
# "normals" and "colors" must have shape (N, 3) and must be on the same
# device as the point cloud.
pcd.point["normals"] = o3d.core.Tensor([[0, 0, 1],
                                        [0, 1, 0],
                                        [1, 0, 0]], dtype, device)
pcd.point["colors"] = o3d.core.Tensor([[0.0, 0.0, 0.0],
                                        [0.1, 0.1, 0.1],
                                        [0.2, 0.2, 0.2]], dtype, device)

# User-defined attributes.
# You can also attach custom attributes. The value tensor must be on the
# same device as the point cloud. The are no restrictions on the shape and
# dtype, e.g.,
pcd.point["intensities"] = o3d.core.Tensor([0.3, 0.1, 0.4], dtype, device)
pcd.point["labels"] = o3d.core.Tensor([3, 1, 4], o3d.core.int32, device)

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: open3d.cpu.pybind.t.geometry.PointCloud, device: open3d.cpu.pybind.core.Device = CPU:0) -> None

Construct an empty pointcloud on the provided device (default: ‘CPU:0’).

__init__(self: open3d.cpu.pybind.t.geometry.PointCloud, positions: open3d.cpu.pybind.core.Tensor) -> None
__init__(self: open3d.cpu.pybind.t.geometry.PointCloud, map_keys_to_tensors: Dict[str, open3d.cpu.pybind.core.Tensor]) -> None

append(self: open3d.cpu.pybind.t.geometry.PointCloud, arg0: open3d.cpu.pybind.t.geometry.PointCloud) → open3d.cpu.pybind.t.geometry.PointCloud¶

clear(self)¶

Clear all elements in the geometry.

Returns: open3d.t.geometry.Geometry

clone(self: open3d.cpu.pybind.t.geometry.PointCloud) → open3d.cpu.pybind.t.geometry.PointCloud¶: Returns a copy of the point cloud on the same device.

cpu(self: open3d.cpu.pybind.t.geometry.PointCloud) → open3d.cpu.pybind.t.geometry.PointCloud¶: Transfer the point cloud to CPU. If the point cloud is already on CPU, no copy will be performed.

static create_from_depth_image(depth, intrinsics, extrinsics=(with default value), depth_scale=1000.0, depth_max=3.0, stride=1, with_normals=False)¶

Factory function to create a pointcloud (with only ‘points’) from a depth image and a camera model.

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.t.geometry.Image) – The input depth image should be a uint16_t image.
intrinsics (open3d.core.Tensor) – Intrinsic parameters of the camera.
extrinsics (open3d.core.Tensor, optional) –
Extrinsic parameters of the camera. Default value:

[[1.0 0.0 0.0 0.0], [0.0 1.0 0.0 0.0], [0.0 0.0 1.0 0.0], [0.0 0.0 0.0 1.0]] Tensor[shape={4, 4}, stride={4, 1}, Float32

()
depth_scale (float, optional, default=1000.0) – The depth is scaled by 1 / depth_scale.
depth_max (float, optional, default=3.0) – Truncated at depth_max distance.
stride (int, optional, default=1) – Sampling factor to support coarse point cloud extraction. Unless normals are requested, there is no low pass filtering, so aliasing is possible for stride>1.
with_normals (bool, optional, default=False) – Also compute normals for the point cloud. If True, the point cloud will only contain points with valid normals. If normals are requested, the depth map is first filtered to ensure smooth normals.

Returns

open3d.t.geometry.PointCloud

static create_from_rgbd_image(rgbd_image, intrinsics, extrinsics=(with default value), depth_scale=1000.0, depth_max=3.0, stride=1, with_normals=False)¶

Factory function to create a pointcloud (with properties {‘points’, ‘colors’}) from an RGBD image and a camera model.

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

rgbd_image (open3d.t.geometry.RGBDImage) – The input RGBD image should have a uint16_t depth image and RGB image with any DType and the same size.
intrinsics (open3d.core.Tensor) – Intrinsic parameters of the camera.
extrinsics (open3d.core.Tensor, optional) –
Extrinsic parameters of the camera. Default value:

[[1.0 0.0 0.0 0.0], [0.0 1.0 0.0 0.0], [0.0 0.0 1.0 0.0], [0.0 0.0 0.0 1.0]] Tensor[shape={4, 4}, stride={4, 1}, Float32

()
depth_scale (float, optional, default=1000.0) – The depth is scaled by 1 / depth_scale.
depth_max (float, optional, default=3.0) – Truncated at depth_max distance.
stride (int, optional, default=1) – Sampling factor to support coarse point cloud extraction. Unless normals are requested, there is no low pass filtering, so aliasing is possible for stride>1.
with_normals (bool, optional, default=False) – Also compute normals for the point cloud. If True, the point cloud will only contain points with valid normals. If normals are requested, the depth map is first filtered to ensure smooth normals.

Returns

open3d.t.geometry.PointCloud

cuda(self: open3d.cpu.pybind.t.geometry.PointCloud, device_id: int = 0) → open3d.cpu.pybind.t.geometry.PointCloud¶: Transfer the point cloud to a CUDA device. If the point cloud is already on the specified CUDA device, no copy will be performed.

estimate_color_gradients(self: open3d.cpu.pybind.t.geometry.PointCloud, max_nn: int = 30, radius: Optional[float] = None) → None¶: Function to estimate point color gradients. If radius is provided, then HybridSearch is used, otherwise KNN-Search is used.

estimate_normals(self, max_nn=30, radius=None)¶

Function to estimate point normals. If the pointcloud normals exists, the estimated normals are oriented with respect to the same. It uses KNN search if only max_nn parameter is provided, and HybridSearch if radius parameter is also provided.

Parameters

max_nn (int, optional, default=30) – NeighbourSearch max neighbours parameter [default = 30].
radius (Optional[float], optional, default=None) – [optional] NeighbourSearch radius parameter to use HybridSearch. [Recommended ~1.4x voxel size].

Returns

None

static from_legacy(pcd_legacy: open3d.cpu.pybind.geometry.PointCloud, dtype: open3d.cpu.pybind.core.Dtype = Float32, device: open3d.cpu.pybind.core.Device = CPU:0) → open3d.cpu.pybind.t.geometry.PointCloud¶: Create a PointCloud from a legacy Open3D PointCloud.

get_center(self: open3d.cpu.pybind.t.geometry.PointCloud) → open3d.cpu.pybind.core.Tensor¶: Returns the center for point coordinates.

get_max_bound(self: open3d.cpu.pybind.t.geometry.PointCloud) → open3d.cpu.pybind.core.Tensor¶: Returns the max bound for point coordinates.

get_min_bound(self: open3d.cpu.pybind.t.geometry.PointCloud) → open3d.cpu.pybind.core.Tensor¶: Returns the min bound for point coordinates.

has_valid_material(self: open3d.cpu.pybind.t.geometry.DrawableGeometry) → bool¶: Returns true if the geometry’s material is valid.

is_empty(self)¶

Returns True iff the geometry is empty.

Returns: bool

project_to_depth_image()¶

project_to_depth_image(self: open3d.cpu.pybind.t.geometry.PointCloud, width: int, height: int, intrinsics: open3d.cpu.pybind.core.Tensor, extrinsics: open3d.cpu.pybind.core.Tensor = [[1.0 0.0 0.0 0.0],: [0.0 1.0 0.0 0.0], [0.0 0.0 1.0 0.0], [0.0 0.0 0.0 1.0]]

Tensor[shape={4, 4}, stride={4, 1}, Float32, CPU:0, 0x565243d2ca70], depth_scale: float = 1000.0, depth_max: float = 3.0) -> open3d::t::geometry::Image

Project a point cloud to a depth image.

project_to_rgbd_image()¶

project_to_rgbd_image(self: open3d.cpu.pybind.t.geometry.PointCloud, width: int, height: int, intrinsics: open3d.cpu.pybind.core.Tensor, extrinsics: open3d.cpu.pybind.core.Tensor = [[1.0 0.0 0.0 0.0],: [0.0 1.0 0.0 0.0], [0.0 0.0 1.0 0.0], [0.0 0.0 0.0 1.0]]

Tensor[shape={4, 4}, stride={4, 1}, Float32, CPU:0, 0x565243d2d430], depth_scale: float = 1000.0, depth_max: float = 3.0) -> open3d::t::geometry::RGBDImage

Project a colored point cloud to a RGBD image.

rotate(self: open3d.cpu.pybind.t.geometry.PointCloud, R: open3d.cpu.pybind.core.Tensor, center: open3d.cpu.pybind.core.Tensor) → open3d.cpu.pybind.t.geometry.PointCloud¶: Rotate points and normals (if exist).

scale(self: open3d.cpu.pybind.t.geometry.PointCloud, scale: float, center: open3d.cpu.pybind.core.Tensor) → open3d.cpu.pybind.t.geometry.PointCloud¶: Scale points.

to(self: open3d.cpu.pybind.t.geometry.PointCloud, device: open3d.cpu.pybind.core.Device, copy: bool = False) → open3d.cpu.pybind.t.geometry.PointCloud¶: Transfer the point cloud to a specified device.

to_legacy(self: open3d.cpu.pybind.t.geometry.PointCloud) → open3d.cpu.pybind.geometry.PointCloud¶: Convert to a legacy Open3D PointCloud.

transform(self: open3d.cpu.pybind.t.geometry.PointCloud, transformation: open3d.cpu.pybind.core.Tensor) → open3d.cpu.pybind.t.geometry.PointCloud¶: Transforms the points and normals (if exist).

translate(self: open3d.cpu.pybind.t.geometry.PointCloud, translation: open3d.cpu.pybind.core.Tensor, relative: bool = True) → open3d.cpu.pybind.t.geometry.PointCloud¶: Translates points.

voxel_down_sample(self: open3d.cpu.pybind.t.geometry.PointCloud, voxel_size: float) → open3d.cpu.pybind.t.geometry.PointCloud¶: Downsamples a point cloud with a specified voxel size.

property material¶

property point¶

positions, colors, normals, etc.

Type: Point’s attributes