A Python library for common tasks on 3D point clouds

Last update: Dec 27, 2022

Overview

Point Cloud Utils (pcu) - A Python library for common tasks on 3D point clouds

Point Cloud Utils (pcu) is a utility library providing the following functionality. See the Examples section for documentation on how to use these:

Utility functions for reading and writing many common mesh formats (PLY, STL, OFF, OBJ, 3DS, VRML 2.0, X3D, COLLADA). If it can be imported into MeshLab, we can read it!
A series of algorithms for generating point samples on meshes:
- Poisson-Disk-Sampling of a mesh based on "Parallel Poisson Disk Sampling with Spectrum Analysis on Surface".
- Sampling a mesh with Lloyd's algorithm.
- Monte-Carlo sampling on a mesh.
Utilities for downsampling point clouds:
- To satisfy a blue noise distribution
- On a voxel grid
Closest points between a point cloud and a mesh
Normal estimation from point clouds and triangle meshes
Fast k-nearest-neighbor search between point clouds (based on nanoflann).
Hausdorff distances between point-clouds.
Chamfer distances between point-clouds.
Approximate Wasserstein distances between point-clouds using the Sinkhorn method.
Compute signed distances between a point cloud and a mesh using Fast Winding Numbers
Compute closest points on a mesh to a point cloud
Deduplicating point clouds and mesh vertices
Mesh smoothing
Making a mesh watertight (based on the Watertight Manifold algorithm)

Installation Instructions

With `conda`

Simply run:

conda install -c conda-forge point_cloud_utils

With `pip`

pip install git+git://github.com/fwilliams/point-cloud-utils

The following dependencies are required to install with pip:

A C++ compiler supporting C++14 or later
git

Examples

List of examples

Loading meshes and point clouds
Saving meshes and point clouds
Generating blue-noise samples on a mesh with Poisson-disk sampling
Generate random samples on a mesh
Downsample a point cloud to have a blue noise distribution
Downsample a point cloud on a voxel grid
Estimating normals from a point cloud
Approximate Wasserstein (Sinkhorn) distance between two point clouds
Chamfer distance between two point clouds
Hausdorff distance between two point clouds
K-nearest-neighbors between two point clouds
Generating point samples in the square and cube with Lloyd relaxation
Compute shortest signed distances to a triangle mesh with fast winding numbers
Compute closest points on a mesh
Deduplicating point clouds and meshes
Smoothing a mesh
Making a mesh watertight

Loading meshes and point clouds

Point-Cloud-Utils supports reading many common mesh formats (PLY, STL, OFF, OBJ, 3DS, VRML 2.0, X3D, COLLADA). If it can be imported into MeshLab, we can read it! The type of file is inferred from its file extension.

If you only need a few attributes of a point cloud or mesh, the quickest way to load a mesh is using one of the read_mesh_* utility functions

import point_cloud_utils as pcu

# Load vertices and faces for a mesh
v, f = pcu.load_mesh_vf("path/to/mesh")

# Load vertices and per-vertex normals
v, n = pcu.load_mesh_vn("path/to/mesh")

# Load vertices, per-vertex normals, and per-vertex-colors
v, n, c = pcu.load_mesh_vnc("path/to/mesh")

# Load vertices, faces, and per-vertex normals
v, f, n = pcu.load_mesh_vfn("path/to/mesh")

# Load vertices, faces, per-vertex normals, and per-vertex colors
v, f, n, c = pcu.load_mesh_vfnc("path/to/mesh")

For meshes and point clouds with more complex attributes, use load_triangle_mesh which returns a TriangleMesh object.

import point_cloud_utils as pcu

# mesh is a lightweight TriangleMesh container object holding mesh vertices, faces, and their attributes.
# Any attributes which aren't loaded (because they aren't present in the file) are set to None.
# The data in TriangleMesh is layed out as follows (run help(pcu.TriangleMesh) for more details):
# TriangleMesh:
#   vertex_data:
#       positions: [V, 3]-shaped numpy array of per-vertex positions
#       normals: [V, 3]-shaped numpy array of per-vertex normals (or None)
#       texcoords: [V, 2]-shaped numpy array of per-vertex uv coordinates (or None)
#       tex_ids: [V,]-shaped numpy array of integer indices into TriangleMesh.textures indicating which texture to
#                use at this vertex (or None)
#       colors: [V, 4]-shaped numpy array of per-vertex RBGA colors in [0.0, 1.0] (or None)
#       radius: [V,]-shaped numpy array of per-vertex curvature radii (or None)
#       quality: [V,]-shaped numpy array of per-vertex quality measures (or None)
#       flags: [V,]-shaped numpy array of 32-bit integer flags per vertex (or None)
#   face_data:
#       vertex_ids: [F, 3]-shaped numpy array of integer face indices into TrianglMesh.vertex_data.positions
#       normals: [F, 3]-shaped numpy array of per-face normals (or None)
#       colors: [F, 4]-shaped numpy array of per-face RBGA colors in [0.0, 1.0] (or None)
#       quality: [F,]-shaped numpy array of per-face quality measures (or None)
#       flags: [F,]-shaped numpy array of 32-bit integer flags per face (or None)
#
#       wedge_colors: [F, 3, 4]-shaped numpy array of per-wedge RBGA colors in [0.0, 1.0] (or None)
#       wedge_normals: [F, 3, 3]-shaped numpy array of per-wedge normals (or None)
#       wedge_texcoords: [F, 3, 2]-shaped numpy array of per-wedge] uv coordinates (or None)
#       wedge_tex_ids: [F, 3]-shaped numpy array of integer indices into TriangleMesh.textures indicating which
#                      texture to use at this wedge (or None)
#   textures: A list of paths to texture image files for this mesh
#   normal_maps: A list of paths to texture image files for this mesh
mesh = pcu.load_triangle_mesh("path/to/mesh")

# You can also load a mesh directly using the TriangleMesh class
mesh = pcu.TriangleMesh("path/to/mesh")

For meshes and point clouds with more complex attributes, use save_triangle_mesh which accepts a whole host of named arguments which control the attributes to save.

# save_triangle_mesh accepts a path to save to (The type of mesh  saved is determined by the file extesion),
# an array of mesh vertices of shape [V, 3], and optional arguments specifying faces, per-mesh attributes,
# per-face attributes and per-wedge attributes:
#   filename    : Path to the mesh to save. The type of file will be determined from the file extension.
#   v           : [V, 3]-shaped numpy array of per-vertex positions
#   f           : [F, 3]-shaped numpy array of integer face indices into TrianglMesh.vertex_data.positions (or None)
#   vn          : [V, 3]-shaped numpy array of per-vertex normals (or None)
#   vt          : [V, 2]-shaped numpy array of per-vertex uv coordinates (or None)
#   vc          : [V, 4]-shaped numpy array of per-vertex RBGA colors in [0.0, 1.0] (or None)
#   vq          : [V,]-shaped numpy array of per-vertex quality measures (or None)
#   vr          : [V,]-shaped numpy array of per-vertex curvature radii (or None)
#   vti         : [V,]-shaped numpy array of integer indices into TriangleMesh.textures indicating which texture to
#                 use at this vertex (or None)
#   vflags      : [V,]-shaped numpy array of 32-bit integer flags per vertex (or None)
#   fn          : [F, 3]-shaped numpy array of per-face normals (or None)
#   fc          : [F, 4]-shaped numpy array of per-face RBGA colors in [0.0, 1.0] (or None)
#   fq          : [F,]-shaped numpy array of per-face quality measures (or None)
#   fflags      : [F,]-shaped numpy array of 32-bit integer flags per face (or None)
#   wc          : [F, 3, 4]-shaped numpy array of per-wedge RBGA colors in [0.0, 1.0] (or None)
#   wn          : [F, 3, 3]-shaped numpy array of per-wedge normals (or None)
#   wt          : [F, 3, 2]-shaped numpy array of per-wedge] uv coordinates (or None)
#   wti         : [F, 3]-shaped numpy array of integer indices into TriangleMesh.textures indicating which
#   textures    : A list of paths to texture image files for this mesh
#   normal_maps : A list of paths to texture image files for this mesh
pcu.save_triangle_mesh("path/to/mesh", v=v, f=f, vn=vertex_normals, vc=vertex_colors, fn=face_normals)

# You can also directly save a pcu.TrianglMesh object
mesh.save("path/to/mesh")

Saving meshes and point clouds

Point-Cloud-Utils supports writing many common mesh formats (PLY, STL, OFF, OBJ, 3DS, VRML 2.0, X3D, COLLADA). If it can be imported into MeshLab, we can read it! The type of file is inferred from its file extension.

If you only need to write few attributes of a point cloud or mesh, the quickest way to use the save_mesh_* functions

import point_cloud_utils as pcu

# Assume v, f, n, c are numpy arrays
# where
#   v are the mesh vertices of shape [V, 3]
#   f are the mesh face indices into v of shape [F, 3]
#   n are the mesh per-vertex normals of shape [V, 3]
#   c are the mesh per-vertex colors of shape [V, 4]

# Save mesh vertices and faces
pcu.save_mesh_vf("path/to/mesh", v, f)

# Save mesh vertices and per-vertex normals
v, n = pcu.save_mesh_vn("path/to/mesh", v, n)

# Save mesh vertices, per-vertex normals, and per-vertex-colors
v, n, c = pcu.save_mesh_vnc("path/to/mesh", v, n, c)

# Save mesh vertices, faces, and per-vertex normals
v, f, n = pcu.save_mesh_vfn("path/to/mesh", v, f, n)

# Save vertices, faces, per-vertex normals, and per-vertex colors
v, f, n, c = pcu.save_mesh_vfnc("path/to/mesh", v, f, n, c)

Generating blue-noise samples on a mesh with Poisson-disk sampling

Generate 10000 samples on a mesh with poisson disk samples

import point_cloud_utils as pcu
import numpy as np

# v is a nv by 3 NumPy array of vertices
# f is an nf by 3 NumPy array of face indexes into v
# n is a nv by 3 NumPy array of vertex normals
v, f, n = pcu.load_mesh_vfn("my_model.ply")

# Generate 10000 samples on a mesh with poisson disk samples
# f_i are the face indices of each sample and bc are barycentric coordinates of the sample within a face
f_i, bc = pcu.sample_mesh_poisson_disk(v, f, n, 10000)

# Use the face indices and barycentric coordinate to compute sample positions and normals
v_poisson = pcu.interpolate_barycentric_coords(f, fi, bc, v)
n_poisson = pcu.interpolate_barycentric_coords(f, fi, bc, n)

Generate blue noise samples on a mesh separated by approximately 0.01 times the bounding box diagonal

import point_cloud_utils as pcu
import numpy as np
# v is a nv by 3 NumPy array of vertices
# f is an nf by 3 NumPy array of face indexes into v
# n is a nv by 3 NumPy array of vertex normals
v, f, n = pcu.load_mesh_vfn("my_model.ply")


# Generate samples on a mesh with poisson disk samples seperated by approximately 0.01 times
# the length of the bounding box diagonal
bbox = np.max(v, axis=0) - np.min(v, axis=0)
bbox_diag = np.linalg.norm(bbox)

# f_i are the face indices of each sample and bc are barycentric coordinates of the sample within a face
f_i, bc = pcu.sample_mesh_poisson_disk(v, f, n, 10000)

# Use the face indices and barycentric coordinate to compute sample positions and normals
v_sampled = pcu.interpolate_barycentric_coords(f, fi, bc, v)
n_sampled = pcu.interpolate_barycentric_coords(f, fi, bc, n)

Generate random samples on a mesh

import point_cloud_utils as pcu
import numpy as np

# v is a nv by 3 NumPy array of vertices
# f is an nf by 3 NumPy array of face indexes into v
# n is a nv by 3 NumPy array of vertex normals
v, f, n = pcu.load_mesh_vfn("my_model.ply")

# Generate random samples on the mesh (v, f, n)
# f_idx are the face indices of each sample and bc are barycentric coordinates of the sample within a face
f_idx, bc = pcu.sample_mesh_random(v, f, num_samples=v.shape[0] * 40)

# Use the face indices and barycentric coordinate to compute sample positions and normals
v_sampled = pcu.interpolate_barycentric_coords(f, fi, bc, v)
n_sampled = pcu.interpolate_barycentric_coords(f, fi, bc, n)

Downsample a point cloud to have a blue noise distribution

import point_cloud_utils as pcu
import numpy as np

# v is a nv by 3 NumPy array of vertices
# n is a nv by 3 NumPy array of vertex normals
v, n = pcu.load_mesh_vn("my_model.ply")

# Downsample a point cloud by approximately 50% so that the sampled points approximately
# follow a blue noise distribution
# idx is an array of integer indices into v indicating which samples to keep
idx = pcu.downsample_point_cloud_poisson_disk(v, num_samples=int(0.5*v.shape[0]))

# Use the indices to get the sample positions and normals
v_sampled = v[idx]
n_sampled = n[idx]

Downsample a point cloud on a voxel grid

Simple downsampling within the bounding box of a point cloud

import point_cloud_utils as pcu
import numpy as np

# v is a nv by 3 NumPy array of vertices
# n is a nv by 3 NumPy array of vertex normals
# n is a nv by 4 NumPy array of vertex colors
v, n, c = pcu.load_mesh_vnc("my_model.ply")

# We'll use a voxel grid with 128 voxels per axis
num_voxels_per_axis = 128

# Size of the axis aligned bounding box of the point cloud
bbox_size = v.max(0) - v.min(0)

# The size per-axis of a single voxel
sizeof_voxel = bbox_size / num_voxels_per_axis

# Downsample a point cloud on a voxel grid so there is at most one point per voxel.
# Multiple points, normals, and colors within a voxel cell are averaged together.
v_sampled, n_sampled, c_sampled = pcu.downsample_point_cloud_voxel_grid(sizeof_voxel, v, n, c)

Specifying the location of the voxel grid in space (e.g. to only consider points wihtin a sub-region of the point cloud)

import point_cloud_utils as pcu
import numpy as np

# v is a nv by 3 NumPy array of vertices
# n is a nv by 3 NumPy array of vertex normals
# n is a nv by 4 NumPy array of vertex colors
v, n, c = pcu.load_mesh_vnc("my_model.ply")

# We'll use a voxel grid with 128 voxels per axis
num_voxels_per_axis = 128

# Size of the axis aligned bounding box of the point cloud
bbox_size = v.max(0) - v.min(0)

# Let's say we only want to consider points in the top right corner of the bounding box
domain_min = v.min(0) + bbox_size / 2.0
domain_max = v.min(0) + bbox_size

# The size per-axis of a single voxel
sizeof_voxel = bbox_size / num_voxels_per_axis

# Downsample a point cloud on a voxel grid so there is at most one point per voxel.
# Multiple points, normals, and colors within a voxel cell are averaged together.
# min_bound and max_bound specify a bounding box in which we will downsample points
v_sampled, n_sampled, c_sampled = pcu.downsample_point_cloud_voxel_grid(sizeof_voxel, v, n, c,
                                                                        min_bound=domain_min, max_bound=domain_max)

Discarding voxels with too few points

import point_cloud_utils as pcu
import numpy as np

# v is a nv by 3 NumPy array of vertices
# n is a nv by 3 NumPy array of vertex normals
# n is a nv by 4 NumPy array of vertex colors
v, n, c = pcu.load_mesh_vnc("my_model.ply")

# We'll use a voxel grid with 128 voxels per axis
num_voxels_per_axis = 128

# Size of the axis aligned bounding box of the point cloud
bbox_size = v.max(0) - v.min(0)

# The size per-axis of a single voxel
sizeof_voxel = bbox_size / num_voxels_per_axis

# We will throw away points within voxel cells containing fewer than 3 points
min_points_per_voxel = 3

# Downsample a point cloud on a voxel grid so there is at most one point per voxel.
# Multiple points, normals, and colors within a voxel cell are averaged together.
v_sampled, n_sampled, c_sampled = pcu.downsample_point_cloud_voxel_grid(sizeof_voxel, v, n, c,
                                                                        min_points_per_voxel=min_points_per_voxel)

Compute closest points on a mesh

import point_cloud_utils as pcu

# v is a nv by 3 NumPy array of vertices
v, f = pcu.load_mesh_vf("my_model.ply")

# Generate 1000 random query points. We will find the closest point on the mesh for each of these
p = np.random.rand(1000, 3)

# For each query point, find the closest point on the mesh.
# Here:
#  - d is an array of closest distances for each query point with shape (1000,)
#  - fi is an array of closest face indices for each point with shape (1000,)
#  - bc is an array of barycentric coordinates within each face (shape (1000, 3)
#    of the closest point for each query point
d, fi, bc = pcu.closest_points_on_mesh(p, v, f)

# Convert barycentric coordinates to 3D positions
closest_points = pcu.interpolate_barycentric_coords(f, fi, bc, v)

Estimating normals from a point cloud

import point_cloud_utils as pcu

# v is a nv by 3 NumPy array of vertices
v = pcu.load_mesh_v("my_model.ply")

# Estimate a normal at each point (row of v) using its k nearest neighbors
n = pcu.estimate_point_cloud_normals(n, k=16)

Approximate Wasserstein (Sinkhorn) distance between two point clouds

import point_cloud_utils as pcu
import numpy as np

# a and b are arrays where each row contains a point
# Note that the point sets can have different sizes (e.g [100, 3], [111, 3])
a = np.random.rand(100, 3)
b = np.random.rand(100, 3)

# M is a 100x100 array where each entry  (i, j) is the squared distance between point a[i, :] and b[j, :]
M = pcu.pairwise_distances(a, b)

# w_a and w_b are masses assigned to each point. In this case each point is weighted equally.
w_a = np.ones(a.shape[0])
w_b = np.ones(b.shape[0])

# P is the transport matrix between a and b, eps is a regularization parameter, smaller epsilons lead to
# better approximation of the true Wasserstein distance at the expense of slower convergence
P = pcu.sinkhorn(w_a, w_b, M, eps=1e-3)

# To get the distance as a number just compute the frobenius inner product <M, P>
sinkhorn_dist = (M*P).sum()

Chamfer distance between two point clouds

import point_cloud_utils as pcu
import numpy as np

# a and b are arrays where each row contains a point
# Note that the point sets can have different sizes (e.g [100, 3], [111, 3])
a = np.random.rand(100, 3)
b = np.random.rand(100, 3)

chamfer_dist = pcu.chamfer_distance(a, b)

Hausdorff distance between two point clouds

import point_cloud_utils as pcu
import numpy as np

# Generate two random point sets
a = np.random.rand(1000, 3)
b = np.random.rand(500, 3)

# Compute one-sided squared Hausdorff distances
hausdorff_a_to_b = pcu.one_sided_hausdorff_distance(a, b)
hausdorff_b_to_a = pcu.one_sided_hausdorff_distance(b, a)

# Take a max of the one sided squared  distances to get the two sided Hausdorff distance
hausdorff_dist = pcu.hausdorff_distance(a, b)

# Find the index pairs of the two points with maximum shortest distancce
hausdorff_b_to_a, idx_b, idx_a = pcu.one_sided_hausdorff_distance(b, a, return_index=True)
assert np.abs(np.sum((a[idx_a] - b[idx_b])**2) - hausdorff_b_to_a) < 1e-5, "These values should be almost equal"

# Find the index pairs of the two points with maximum shortest distancce
hausdorff_dist, idx_b, idx_a = pcu.hausdorff_distance(b, a, return_index=True)
assert np.abs(np.sum((a[idx_a] - b[idx_b])**2) - hausdorff_dist) < 1e-5, "These values should be almost equal"

K-nearest-neighbors between two point clouds

import point_cloud_utils as pcu
import numpy as np

# Generate two random point sets
a = np.random.rand(1000, 3)
b = np.random.rand(500, 3)

# dists_a_to_b is of shape (a.shape[0],) and contains the shortest squared distance
# between each point in a and the points in b
# corrs_a_to_b is of shape (a.shape[0],) and contains the index into b of the
# closest point for each point in a
dists_a_to_b, corrs_a_to_b = pcu.shortest_distance_pairs(a, b)

Generating point samples in the square and cube with Lloyd relaxation

import point_cloud_utils as pcu

# v is a nv by 3 NumPy array of vertices
# f is an nf by 3 NumPy array of face indexes into v
v, f = pcu.load_mesh_vf("my_model.ply")

# Generate 1000 points on the mesh with Lloyd's algorithm
samples = pcu.sample_mesh_lloyd(v, f, 1000)

# Generate 100 points on the unit square with Lloyd's algorithm
samples_2d = pcu.lloyd_2d(100)

# Generate 100 points on the unit cube with Lloyd's algorithm
samples_3d = pcu.lloyd_3d(100)

Compute shortest signed distances to a triangle mesh with fast winding numbers

import point_cloud_utils as pcu

# v is a nv by 3 NumPy array of vertices
# f is an nf by 3 NumPy array of face indexes into v
v, f = pcu.load_mesh_vf("my_model.ply")

# Generate 1000 points in the volume around the mesh. We'll compute the signed distance to the
# mesh at each of these points
pts = np.random.rand(1000, 3) * (v.max(0) - v.min(0)) + v.min(0)

# Compute the sdf, the index of the closest face in the mesh, and the barycentric coordinates of
# closest point on the mesh, for each point in pts
sdfs, face_ids, barycentric_coords = pcu.signed_distance_to_mesh(pts, v, f)

Deduplicating Point Clouds and Meshes

Point Clouds:

import point_cloud_utils as pcu

# p is a (n, 3)-shaped array of points (one per row)
# p is a (n, 3)-shaped array of normals at each point
p, n = pcu.load_mesh_vn("my_pcloud.ply")

# Treat any points closer than 1e-7 apart as the same point
# idx_i is an array of indices such that p_dedup = p[idx_i]
# idx_j is an array of indices such that p = p_dedup[idx_j]
p_dedup, idx_i, idx_j  = deduplicate_point_cloud(p, 1e-7)

# Use idx_i to deduplicate the normals
n_dedup = n[idx_i]

Meshes:

# v is a (nv, 3)-shaped NumPy array of vertices
# f is an (nf, 3)-shaped NumPy array of face indexes into v
# c is a (nv, 4)-shaped numpy array of per-vertex colors
v, f, c = pcu.load_mesh_vfc("my_model.ply")

# Treat any points closer than 1e-7 apart as the same point
# idx_i is an array of indices such that v_dedup = v[idx_i]
# idx_j is an array of indices such that v = v_dedup[idx_j]
v_dedup, f_dedup, idx_i, idx_j = pcu.deduplicate_mesh_vertices(v, f, 1e-7)

# Use idx_i to deduplicate the colors
c_dedup = c[idx_i]

Smoothing a Mesh

import point_cloud_utils as pcu

# v is a nv by 3 NumPy array of vertices
# f is an nf by 3 NumPy array of face indexes into v
v, f = pcu.load_mesh_vf("my_model.ply")

num_iters = 3  # Number of smoothing iterations
use_cotan_weights = True  # Whether to use cotangent weighted laplacian

# vsmooth contains the vertices of the smoothed mesh (the new mesh has the same face indices f)
vsmooth = pcu.laplacian_smooth_mesh(v, f, num_iters, use_cotan_weights=use_cotan_weights)

Making a Mesh Watertight

import point_cloud_utils as pcu

# v is a nv by 3 NumPy array of vertices
# f is an nf by 3 NumPy array of face indexes into v
v, f = pcu.load_mesh_vf("my_model.ply")

# Optional resolution parameter (default is 20_000).
# See https://github.com/hjwdzh/Manifold for details
resolution = 20_000  
v_watertight, f_watertight = pcu.make_mesh_watertight(v, f, resolution=resolution)

Comments

Pip3 install point_cloud_utils Error

Thank you very much for your great work. However, I met some problems when I run pip3 install point_cloud_utils. We would be grateful if you could help us to find the problem!

Here is the output in the console:

Building wheel for point-cloud-utils (pyproject.toml) ... error
  ERROR: Command errored out with exit status 1:
   command: /usr/bin/python3 /home/orca/.local/lib/python3.6/site-packages/pip/_vendor/pep517/in_process/_in_process.py build_wheel /tmp/tmpqzzsiv3a
       cwd: /tmp/pip-install-9dj02x4j/point-cloud-utils_3880d41467d44289b8d9193e8f62e767
  Complete output (60 lines):
  running bdist_wheel
  running build
  running build_py
  creating build
  creating build/lib.linux-aarch64-3.6
  creating build/lib.linux-aarch64-3.6/point_cloud_utils
  copying point_cloud_utils/__init__.py -> build/lib.linux-aarch64-3.6/point_cloud_utils
  copying point_cloud_utils/_octree.py -> build/lib.linux-aarch64-3.6/point_cloud_utils
  copying point_cloud_utils/_mesh_io.py -> build/lib.linux-aarch64-3.6/point_cloud_utils
  copying point_cloud_utils/_ray_mesh_intersector.py -> build/lib.linux-aarch64-3.6/point_cloud_utils
  copying point_cloud_utils/_pointcloud_normals.py -> build/lib.linux-aarch64-3.6/point_cloud_utils
  copying point_cloud_utils/_sinkhorn.py -> build/lib.linux-aarch64-3.6/point_cloud_utils
  copying point_cloud_utils/_ray_mesh.py -> build/lib.linux-aarch64-3.6/point_cloud_utils
  running build_ext
  CMake Error: The source directory "/tmp/pip-install-9dj02x4j/point-cloud-utils_3880d41467d44289b8d9193e8f62e767" does not appear to contain CMakeLists.txt.
  Specify --help for usage, or press the help button on the CMake GUI.
  Traceback (most recent call last):
    File "/home/orca/.local/lib/python3.6/site-packages/pip/_vendor/pep517/in_process/_in_process.py", line 363, in <module>
      main()
    File "/home/orca/.local/lib/python3.6/site-packages/pip/_vendor/pep517/in_process/_in_process.py", line 345, in main
      json_out['return_val'] = hook(**hook_input['kwargs'])
    File "/home/orca/.local/lib/python3.6/site-packages/pip/_vendor/pep517/in_process/_in_process.py", line 262, in build_wheel
      metadata_directory)
    File "/usr/local/lib/python3.6/dist-packages/setuptools/build_meta.py", line 231, in build_wheel
      wheel_directory, config_settings)
    File "/usr/local/lib/python3.6/dist-packages/setuptools/build_meta.py", line 215, in _build_with_temp_dir
      self.run_setup()
    File "/usr/local/lib/python3.6/dist-packages/setuptools/build_meta.py", line 158, in run_setup
      exec(compile(code, __file__, 'exec'), locals())
    File "setup.py", line 116, in <module>
      main()
    File "setup.py", line 111, in main
      test_suite="tests"
    File "/usr/local/lib/python3.6/dist-packages/setuptools/__init__.py", line 153, in setup
      return distutils.core.setup(**attrs)
    File "/usr/lib/python3.6/distutils/core.py", line 148, in setup
      dist.run_commands()
    File "/usr/lib/python3.6/distutils/dist.py", line 955, in run_commands
      self.run_command(cmd)
    File "/usr/lib/python3.6/distutils/dist.py", line 974, in run_command
      cmd_obj.run()
    File "/tmp/pip-build-env-4fp4vm8t/overlay/lib/python3.6/site-packages/wheel/bdist_wheel.py", line 299, in run
      self.run_command('build')
    File "/usr/lib/python3.6/distutils/cmd.py", line 313, in run_command
      self.distribution.run_command(command)
    File "/usr/lib/python3.6/distutils/dist.py", line 974, in run_command
      cmd_obj.run()
    File "/usr/lib/python3.6/distutils/command/build.py", line 135, in run
      self.run_command(cmd_name)
    File "/usr/lib/python3.6/distutils/cmd.py", line 313, in run_command
      self.distribution.run_command(command)
    File "/usr/lib/python3.6/distutils/dist.py", line 974, in run_command
      cmd_obj.run()
    File "setup.py", line 39, in run
      self.build_extension(ext)
    File "setup.py", line 71, in build_extension
      subprocess.check_call(['cmake'] + cmake_args + [ext.sourcedir], cwd=self.build_temp, env=env)
    File "/usr/lib/python3.6/subprocess.py", line 311, in check_call
      raise CalledProcessError(retcode, cmd)
  subprocess.CalledProcessError: Command '['cmake', '-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=/tmp/pip-install-9dj02x4j/point-cloud-utils_3880d41467d44289b8d9193e8f62e767/build/lib.linux-aarch64-3.6/point_cloud_utils', '-DPYTHON_EXECUTABLE=/usr/bin/python3', '-DCMAKE_BUILD_TYPE=Release', '/tmp/pip-install-9dj02x4j/point-cloud-utils_3880d41467d44289b8d9193e8f62e767']' returned non-zero exit status 1.
  ----------------------------------------
  ERROR: Failed building wheel for point-cloud-utils
Failed to build point-cloud-utils
ERROR: Could not build wheels for point-cloud-utils, which is required to install pyproject.toml-based projects

opened by LilyGinger 15

Compilation error on windows

Compilation on windows stops with error

CMake Error at external/numpyeigen/cmake/numpyeigen.cmake:85 (warning):
    Unknown CMake command "warning".

opened by nicolocarissimi 10

pcu.write_ply() throws "Incompatible function arguments error"
I am trying to load a mesh in .obj format, perform Lloyd Relaxation and then save the point clouds to .ply format. I am writing the following code:

v,f,n = pcu.read_obj('./data/ChairNoArms9.obj') samples = pcu.sample_mesh_lloyd(v, f, 15000) pcu.write_ply(filename='sample.ply', v=samples, f=f, ascii=True)

Note: I have no normals in my mesh file
opened by cravisjan97 9
program end with exit code -1073741819 (0xC0000005)

Hi I use pcu for sampling a ply file. It shows Process finished with exit code -1073741819 (0xC0000005) in the middle. But there is no such thing when I debug it. Python version 3.7.6 Win10

opened by bearinsuke 9
Invalid Mesh input error

what is reason for value error in python3.6 as valueError: Invalid input mesh with zero elements: v and f must have shape (n, 3) and (m, 3) (n, m > 0). Got v.shape =(119844, 3), f.shape = (0, 0).

The PLY is opening good in other programs.

opened by sonumathur 6
Return barycentric coordinates in mesh sampling functions

Currently the mesh sampling functions return points (and possibly normals) which is limiting if the user has attributes stored at mesh vertices. Let's update them to optionally return barycentric coordinates.

opened by fwilliams 6
install successfully, but not find attributes

import point_cloud_utils as pcu pcu. pcu.class( pcu.eq( pcu.gt( pcu.loader pcu.package pcu.setattr(
pcu.delattr( pcu.file pcu.hash( pcu.lt( pcu.path pcu.sizeof(
pcu.dict pcu.format( pcu.init( pcu.name pcu.reduce( pcu.spec
pcu.dir( pcu.ge( pcu.init_subclass( pcu.ne( pcu.reduce_ex( pcu.str(
pcu.doc pcu.getattribute( pcu.le( pcu.new( pcu.repr( pcu.subclasshook(
v, f, n, _ = pcu.read_ply("model.ply") Traceback (most recent call last): File "", line 1, in AttributeError: module 'point_cloud_utils' has no attribute 'read_ply'

I installed it by 'python3 setup.py install --user'.

opened by ghost 6
AttributeError: module 'point_cloud_utils' has no attribute 'sample'

AttributeError: module 'point_cloud_utils' has no attribute 'sample' I am trying to use the given example for mesh sampling. And it raises the error above.

opened by lelouedec 6
Would it be possible to make this code work natively for CUDA pytorch tensors?

I appreciate it might be difficult to program but I'm currently creating a loss function for my autoencoder by turning a pytorch tensor into numpy, calculating indicies of nearest neighbour pairs and then just indexing the tensor to get a differentiable loss function from it.

Would it be possible to make this algorithm natively compatible with pytorch cuda tensors to immediatly get a differentiable loss function between two point clouds?

opened by mm04926412 5

read_obj: file not found

Thanks for your great work!

Warning: readOBJ() ignored non-comment line 155880:
  o mesh1.002_mesh1-geometry
Failed to cast FTC to matrix: min (0) != max (3)
Traceback (most recent call last):
  File "prepare_complete.py", line 49, in <module>
    pcd = sampling_from_mesh(mesh_path, pcd_path, num_points, sample_method)
  File "../sampling/sample.py", line 62, in sampling_from_mesh
    v, f, n = pcu.read_obj(mesh)
ValueError: File '/model.obj' not found.

but the model.obj does exists! what happened?

opened by Leerw 5

In fast k-nearest-neighbor search, do you use approximate methods?

Hello! The term "fast" made me confused a little. Does it mean that the nearest neighbors are searched using approximate methods? It will be nice if you can provide some information about this function.

opened by nimajam41 3
compiling error on M1 Macbook pro

when pip install point_cloud_utils

got this error: CMake Error: The source directory "/private/var/folders/_1/z9htdvys25dbgnl4nfxmvhf80000gn/T/pip-install-ihjh_3ei/point-cloud-utils_55486fa531a24a6886398db7d72c948d" does not appear to contain CMakeLists.txt.

opened by usccolumbia 0

Issues building on MacBook M1

I've tried pip install -vvv point-cloud-utils and building from source. Here's the error log:

[2022-05-20 14:06:51] ~/Documents/repos/point-cloud-utils $ cmake --version
cmake version 3.21.1

CMake suite maintained and supported by Kitware (kitware.com/cmake).

[2022-05-20 14:05:36] ~/Documents/repos/point-cloud-utils $ python setup.py build
running build
running build_py
creating build
creating build/lib.macosx-11.0-arm64-cpython-39
creating build/lib.macosx-11.0-arm64-cpython-39/point_cloud_utils
copying point_cloud_utils/_ray_mesh_intersector.py -> build/lib.macosx-11.0-arm64-cpython-39/point_cloud_utils
copying point_cloud_utils/_mesh_io.py -> build/lib.macosx-11.0-arm64-cpython-39/point_cloud_utils
copying point_cloud_utils/__init__.py -> build/lib.macosx-11.0-arm64-cpython-39/point_cloud_utils
copying point_cloud_utils/_octree.py -> build/lib.macosx-11.0-arm64-cpython-39/point_cloud_utils
copying point_cloud_utils/_pointcloud_normals.py -> build/lib.macosx-11.0-arm64-cpython-39/point_cloud_utils
copying point_cloud_utils/_ray_mesh.py -> build/lib.macosx-11.0-arm64-cpython-39/point_cloud_utils
copying point_cloud_utils/_sinkhorn.py -> build/lib.macosx-11.0-arm64-cpython-39/point_cloud_utils
running build_ext
-- The C compiler identification is AppleClang 12.0.5.12050022
-- The CXX compiler identification is AppleClang 12.0.5.12050022
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /Library/Developer/CommandLineTools/usr/bin/cc - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /Library/Developer/CommandLineTools/usr/bin/c++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
CMake Deprecation Warning at CMakeLists.txt:4 (cmake_minimum_required):
  Compatibility with CMake < 2.8.12 will be removed from a future version of
  CMake.

  Update the VERSION argument <min> value or use a ...<max> suffix to tell
  CMake that the project does not need compatibility with older versions.


Cloning into 'numpyeigen'...
HEAD is now at 4916d92 update pybind11
-- Performing Test COMPILER_SUPPORT_OPENMP
-- Performing Test COMPILER_SUPPORT_OPENMP - Failed
CMake Warning (dev) at external/numpyeigen/cmake/numpyeigen.cmake:98 (set):
  Cannot set "NPE_ROOT_DIR": current scope has no parent.
Call Stack (most recent call first):
  CMakeLists.txt:21 (include)
This warning is for project developers.  Use -Wno-dev to suppress it.

CMake Deprecation Warning at CMakeLists.txt:4 (cmake_minimum_required):
  Compatibility with CMake < 2.8.12 will be removed from a future version of
  CMake.

  Update the VERSION argument <min> value or use a ...<max> suffix to tell
  CMake that the project does not need compatibility with older versions.


Cloning into 'eigen'...
HEAD is now at 21ae2afd4 bump to 3.3.7
CMake Deprecation Warning at CMakeLists.txt:4 (cmake_minimum_required):
  Compatibility with CMake < 2.8.12 will be removed from a future version of
  CMake.

  Update the VERSION argument <min> value or use a ...<max> suffix to tell
  CMake that the project does not need compatibility with older versions.


Cloning into 'pybind11'...
Switched to a new branch 'numpy_hacks_stable'
HEAD is now at aeda673 Hacks for numpyeigen
-- pybind11 v2.9.0 
-- Found PythonInterp: /opt/homebrew/Caskroom/miniforge/base/envs/rnb/bin/python (found version "3.9.12") 
-- Found PythonLibs: /opt/homebrew/Caskroom/miniforge/base/envs/rnb/lib/libpython3.9.dylib
-- Performing Test HAS_FLTO
-- Performing Test HAS_FLTO - Success
-- Performing Test HAS_FLTO_THIN
-- Performing Test HAS_FLTO_THIN - Success
CMake Deprecation Warning at CMakeLists.txt:4 (cmake_minimum_required):
  Compatibility with CMake < 2.8.12 will be removed from a future version of
  CMake.

  Update the VERSION argument <min> value or use a ...<max> suffix to tell
  CMake that the project does not need compatibility with older versions.


Cloning into 'manifold'...
HEAD is now at 81fd342 Update README.md
CMake Deprecation Warning at CMakeLists.txt:4 (cmake_minimum_required):
  Compatibility with CMake < 2.8.12 will be removed from a future version of
  CMake.

  Update the VERSION argument <min> value or use a ...<max> suffix to tell
  CMake that the project does not need compatibility with older versions.


Cloning into 'embree'...
HEAD is now at 69bd4c272 regenerated documentation
-- Found Git: /opt/homebrew/bin/git (found version "2.35.1") 
CMake Deprecation Warning at external/embree/CMakeLists.txt:64 (cmake_policy):
  The OLD behavior for policy CMP0072 will be removed from a future version
  of CMake.

  The cmake-policies(7) manual explains that the OLD behaviors of all
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-- Detecting default ISA...
-- Detected default ISA: SSE2
-- Looking for pthread.h
-- Looking for pthread.h - found
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success
-- Found Threads: TRUE  
CMake Deprecation Warning at external/geogram/CMakeLists.txt:9 (cmake_minimum_required):
  Compatibility with CMake < 2.8.12 will be removed from a future version of
  CMake.

  Update the VERSION argument <min> value or use a ...<max> suffix to tell
  CMake that the project does not need compatibility with older versions.


-- Using local options file: /Users/exing/Documents/repos/point-cloud-utils/external/geogram/CMakeOptions.txt
-- Configuring build for standalone Geogram (without Vorpaline)
-- Doxygen >= 1.7.0 not found, cannot generate documentation
CMake Deprecation Warning at external/geogram/doc/CMakeLists.txt:7 (cmake_minimum_required):
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  CMake.

  Update the VERSION argument <min> value or use a ...<max> suffix to tell
  CMake that the project does not need compatibility with older versions.


-- Found OpenMP_C: -Xclang -fopenmp (found version "5.0") 
-- Found OpenMP_CXX: -Xclang -fopenmp (found version "5.0") 
-- Found OpenMP: TRUE (found version "5.0")  
-- Configuring done
-- Generating done
-- Build files have been written to: /Users/exing/Documents/repos/point-cloud-utils/build/temp.macosx-11.0-arm64-cpython-39
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#define VERSION_INFO \"0.23.0\" -DWITH_OMP 
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#define VERSION_INFO \"0.23.0\" -DWITH_OMP 
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clang: warning: argument unused during compilation: '-msse2' [-Wunused-command-line-argument]
In file included from /Users/exing/Documents/repos/point-cloud-utils/external/embree/common/sys/sysinfo.cpp:4:
/Users/exing/Documents/repos/point-cloud-utils/external/embree/common/sys/sysinfo.h:63:2: error: Unknown ISA
#error Unknown ISA
 ^
In file included from /Users/exing/Documents/repos/point-cloud-utils/external/embree/common/sys/sysinfo.cpp:5:
In file included from /Users/exing/Documents/repos/point-cloud-utils/external/embree/common/sys/intrinsics.h:12:
In file included from /Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/immintrin.h:15:
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:33:5: error: use of undeclared identifier '__builtin_ia32_emms'; did you mean '__builtin_isless'?
    __builtin_ia32_emms();
    ^
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:33:5: note: '__builtin_isless' declared here
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:33:25: error: too few arguments to function call, expected 2, have 0
    __builtin_ia32_emms();
                        ^
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:50:19: error: use of undeclared identifier '__builtin_ia32_vec_init_v2si'
    return (__m64)__builtin_ia32_vec_init_v2si(__i, 0);
                  ^
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:67:12: error: use of undeclared identifier '__builtin_ia32_vec_ext_v2si'
    return __builtin_ia32_vec_ext_v2si((__v2si)__m, 0);
           ^
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:129:19: error: use of undeclared identifier '__builtin_ia32_packsswb'
    return (__m64)__builtin_ia32_packsswb((__v4hi)__m1, (__v4hi)__m2);
                  ^
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:159:19: error: use of undeclared identifier '__builtin_ia32_packssdw'
    return (__m64)__builtin_ia32_packssdw((__v2si)__m1, (__v2si)__m2);
                  ^
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:189:19: error: use of undeclared identifier '__builtin_ia32_packuswb'
    return (__m64)__builtin_ia32_packuswb((__v4hi)__m1, (__v4hi)__m2);
                  ^
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:216:19: error: use of undeclared identifier '__builtin_ia32_punpckhbw'
    return (__m64)__builtin_ia32_punpckhbw((__v8qi)__m1, (__v8qi)__m2);
                  ^
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:239:19: error: use of undeclared identifier '__builtin_ia32_punpckhwd'
    return (__m64)__builtin_ia32_punpckhwd((__v4hi)__m1, (__v4hi)__m2);
                  ^
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:260:19: error: use of undeclared identifier '__builtin_ia32_punpckhdq'
    return (__m64)__builtin_ia32_punpckhdq((__v2si)__m1, (__v2si)__m2);
                  ^
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:287:19: error: use of undeclared identifier '__builtin_ia32_punpcklbw'
    return (__m64)__builtin_ia32_punpcklbw((__v8qi)__m1, (__v8qi)__m2);
                  ^
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:310:19: error: use of undeclared identifier '__builtin_ia32_punpcklwd'
    return (__m64)__builtin_ia32_punpcklwd((__v4hi)__m1, (__v4hi)__m2);
                  ^
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:331:19: error: use of undeclared identifier '__builtin_ia32_punpckldq'
    return (__m64)__builtin_ia32_punpckldq((__v2si)__m1, (__v2si)__m2);
                  ^
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:352:19: error: use of undeclared identifier '__builtin_ia32_paddb'; did you mean '__builtin_arm_addg'?
    return (__m64)__builtin_ia32_paddb((__v8qi)__m1, (__v8qi)__m2);
                  ^
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:352:19: note: '__builtin_arm_addg' declared here
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:352:19: error: first argument of MTE builtin function must be a pointer ('__v8qi' (vector of 8 'char' values) invalid)
    return (__m64)__builtin_ia32_paddb((__v8qi)__m1, (__v8qi)__m2);
                  ^                    ~~~~~~~~~~~~
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:373:19: error: use of undeclared identifier '__builtin_ia32_paddw'; did you mean '__builtin_arm_addg'?
    return (__m64)__builtin_ia32_paddw((__v4hi)__m1, (__v4hi)__m2);
                  ^
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:352:19: note: '__builtin_arm_addg' declared here
    return (__m64)__builtin_ia32_paddb((__v8qi)__m1, (__v8qi)__m2);
                  ^
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:373:19: error: first argument of MTE builtin function must be a pointer ('__v4hi' (vector of 4 'short' values) invalid)
    return (__m64)__builtin_ia32_paddw((__v4hi)__m1, (__v4hi)__m2);
                  ^                    ~~~~~~~~~~~~
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:394:19: error: use of undeclared identifier '__builtin_ia32_paddd'; did you mean '__builtin_arm_addg'?
    return (__m64)__builtin_ia32_paddd((__v2si)__m1, (__v2si)__m2);
                  ^
/Library/Developer/CommandLineTools/usr/lib/clang/12.0.5/include/mmintrin.h:352:19: note: '__builtin_arm_addg' declared here
    return (__m64)__builtin_ia32_paddb((__v8qi)__m1, (__v8qi)__m2);
                  ^
fatal error: too many errors emitted, stopping now [-ferror-limit=]
20 errors generated.
make[2]: *** [embree/common/sys/CMakeFiles/sys.dir/sysinfo.cpp.o] Error 1
make[1]: *** [embree/common/sys/CMakeFiles/sys.dir/all] Error 2
make[1]: *** Waiting for unfinished jobs....
[ 12%] Building CXX object geogram/src/lib/geogram/third_party/CMakeFiles/geogram_third_party.dir/HLBFGS/ICFS.cpp.o
[ 12%] Building CXX object geogram/src/lib/geogram/third_party/CMakeFiles/geogram_third_party.dir/HLBFGS/LineSearch.cpp.o
[ 13%] Building CXX object geogram/src/lib/geogram/third_party/CMakeFiles/geogram_third_party.dir/HLBFGS/Lite_Sparse_Matrix.cpp.o
[ 13%] Linking CXX static library ../../../../../lib/libgeogram_third_party.a
[ 13%] Built target geogram_third_party
make: *** [all] Error 2
Traceback (most recent call last):
  File "/Users/exing/Documents/repos/point-cloud-utils/setup.py", line 116, in <module>
    main()
  File "/Users/exing/Documents/repos/point-cloud-utils/setup.py", line 89, in main
    setuptools.setup(
  File "/opt/homebrew/Caskroom/miniforge/base/envs/rnb/lib/python3.9/site-packages/setuptools/__init__.py", line 87, in setup
    return distutils.core.setup(**attrs)
  File "/opt/homebrew/Caskroom/miniforge/base/envs/rnb/lib/python3.9/site-packages/setuptools/_distutils/core.py", line 148, in setup
    return run_commands(dist)
  File "/opt/homebrew/Caskroom/miniforge/base/envs/rnb/lib/python3.9/site-packages/setuptools/_distutils/core.py", line 163, in run_commands
    dist.run_commands()
  File "/opt/homebrew/Caskroom/miniforge/base/envs/rnb/lib/python3.9/site-packages/setuptools/_distutils/dist.py", line 967, in run_commands
    self.run_command(cmd)
  File "/opt/homebrew/Caskroom/miniforge/base/envs/rnb/lib/python3.9/site-packages/setuptools/dist.py", line 1229, in run_command
    super().run_command(command)
  File "/opt/homebrew/Caskroom/miniforge/base/envs/rnb/lib/python3.9/site-packages/setuptools/_distutils/dist.py", line 986, in run_command
    cmd_obj.run()
  File "/opt/homebrew/Caskroom/miniforge/base/envs/rnb/lib/python3.9/site-packages/setuptools/_distutils/command/build.py", line 136, in run
    self.run_command(cmd_name)
  File "/opt/homebrew/Caskroom/miniforge/base/envs/rnb/lib/python3.9/site-packages/setuptools/_distutils/cmd.py", line 313, in run_command
    self.distribution.run_command(command)
  File "/opt/homebrew/Caskroom/miniforge/base/envs/rnb/lib/python3.9/site-packages/setuptools/dist.py", line 1229, in run_command
    super().run_command(command)
  File "/opt/homebrew/Caskroom/miniforge/base/envs/rnb/lib/python3.9/site-packages/setuptools/_distutils/dist.py", line 986, in run_command
    cmd_obj.run()
  File "/Users/exing/Documents/repos/point-cloud-utils/setup.py", line 39, in run
    self.build_extension(ext)
  File "/Users/exing/Documents/repos/point-cloud-utils/setup.py", line 72, in build_extension
    subprocess.check_call(['cmake', '--build', '.'] + build_args, cwd=self.build_temp)
  File "/opt/homebrew/Caskroom/miniforge/base/envs/rnb/lib/python3.9/subprocess.py", line 373, in check_call
    raise CalledProcessError(retcode, cmd)

opened by etaoxing 2

Fix Compilation on ARM

It seems like -m64 is an x86 tag. See (compilation error cc: error: unrecognized command line option ‘-m64' in #40 ). This appears to be breaking ARM builds.

opened by fwilliams 19
the principle of distance calculation of point cloud to mesh

I can't understand your algorithm that how to use the fast winding number to calculate the distance from the point cloud to the mesh, (I am not very familiar with the C++ , I can only understand the calculation process of calculating the winding number in Gavin's paper)

can you briefly describe the principle of distance calculation or recommand some paper to explain it.Thank you

opened by minty688 1
Is your library extendable for N-dimensional points?

It's actually a feature request! I was trying to use distance metrics like Hausdorff or Chamfer on N-dimensional point sets and as I expected, an exception was thrown. Are these functions extendable for N-dimensional point sets? So that they can compute the distance between extracted features in deep networks like Pointnet?

opened by nimajam41 1

Releases(v0.28.1)

v0.28.1(Jan 4, 2023)

I forgot to bump the release number so doing that now. This is basically 0.28.0.
Source code(tar.gz)
Source code(zip)
v0.28.0(Jan 1, 2023)

Completely rewrite function docstrings. Add mkdocs documentation page. Small API tweaks and fixes.
Source code(tar.gz)
Source code(zip)
v0.27.0(Aug 19, 2022)

Fast winding numbers for triangle soups Mesh face normal estimation Mesh face consistent orientation Fix bug in connected components Add new docs and fix bugs in doc examples Add many new tests
Source code(tar.gz)
Source code(zip)
v0.25.1(Jul 24, 2022)

Fix setup.py
Source code(tar.gz)
Source code(zip)
v0.25.0(Jul 24, 2022)

Bugfixes, mesh decimation, curvature, more tests, updated README
Source code(tar.gz)
Source code(zip)
v0.24.2(Jul 24, 2022)
Function for decimating triangle meshes

Function for removing unreferenced vertices in a mesh

Update documentation and README

Source code(tar.gz)
Source code(zip)
v0.24.1(Jul 20, 2022)

Fix bug in new PLY saving. Add test for PLY loading/saving
Source code(tar.gz)
Source code(zip)
v0.24.0(Jul 7, 2022)

New PLY loader based on tinyply which can handle custom vertex and face attributes. A bunch of small mesh loading improvements (e.g. being able to use float32) Point splatting (surfel) ray tracer and geometry generator
Source code(tar.gz)
Source code(zip)
v0.23.1(May 10, 2022)

MacOS support
Source code(tar.gz)
Source code(zip)
v0.22.0(May 1, 2022)
Add RayMeshIntersector class for doing many queries on a single mesh

Update CI to push to point-cloud-utils (wohoo, I got the name on PyPI!)

Source code(tar.gz)
Source code(zip)
v0.21.11(Mar 11, 2022)

Fix some bugs in normal estimation. Get rid of pointless copies.
Source code(tar.gz)
Source code(zip)
v0.21.8(Feb 16, 2022)

Fixes bugs in sparse voxel grids implementation in 0.21.7 (which is pretty broken)
Source code(tar.gz)
Source code(zip)
v0.21.7(Feb 16, 2022)

Sparse voxel grids Sparse marching cubes Add parallelism in a large number of functions
Source code(tar.gz)
Source code(zip)
v0.21.4(Feb 3, 2022)

Rename package for pypi
Source code(tar.gz)
Source code(zip)
v0.21.3(Jan 31, 2022)
Update pybind11

Fix pipy push

Update readme

Source code(tar.gz)
Source code(zip)
v0.21.2(Jan 26, 2022)

PyPi packaging
Source code(tar.gz)
Source code(zip)
0.21.2(Jan 26, 2022)

Build system which pushes to pipy. New code for estimating normals which can take into account view directions.
Source code(tar.gz)
Source code(zip)
0.19.0(Dec 13, 2021)
Manifold

Embree ray intersection

Fast winding numbers

Source code(tar.gz)
Source code(zip)
0.18.0(Jun 4, 2021)

Source code(tar.gz)
Source code(zip)
0.17.2(Mar 11, 2021)

No real updates to numpyeigen, but it now uses the ssh URL to clone the eigen dependency which should fix CI issues on conda forge.
Source code(tar.gz)
Source code(zip)
0.17.1(Mar 11, 2021)
All the mesh sampling function (except lloyd which uses Geogram) return barycentric coordinates and vertex indices instead points. This lets us sample arbitrary per-vertex attributes using these functions.

Added downsample_point_cloud_voxel_grid function which downsamples a point cloud by averaging points within a voxel grid.

Renamed prune_point_cloud_poisson_disk to downsample_point_cloud_poisson_disk to have consistent naming convention for functions

Renamed estimate_normals to estimate_point_cloud_normals again for consistent naming convention

Source code(tar.gz)
Source code(zip)
0.16.0(Oct 14, 2020)
Fix version of numpyeigen so that build passes on all platforms and python versions. Make API for prune_pointcloud_poisson_disk consistent with sample_mesh_poisson_disk.

In particular add random seed and option to sample by number or radius

Source code(tar.gz)
Source code(zip)
0.15.1(May 6, 2020)

Source code(tar.gz)
Source code(zip)
0.15.0(May 5, 2020)

Source code(tar.gz)
Source code(zip)
0.13.0(Feb 5, 2020)

Source code(tar.gz)
Source code(zip)
0.12.2(Feb 4, 2020)

Source code(tar.gz)
Source code(zip)
0.12.1(Feb 4, 2020)

Source code(tar.gz)
Source code(zip)
0.12.0(Nov 21, 2019)

Source code(tar.gz)
Source code(zip)
0.11.1(Nov 20, 2019)

Small bug in pairwise distances for non-even norms.
Source code(tar.gz)
Source code(zip)
0.11.0(Nov 18, 2019)

Source code(tar.gz)
Source code(zip)

A Python library for common tasks on 3D point clouds

Related tags

Overview

Point Cloud Utils (pcu) - A Python library for common tasks on 3D point clouds

Installation Instructions

With conda

With pip

Examples

List of examples

Loading meshes and point clouds

Saving meshes and point clouds

Generating blue-noise samples on a mesh with Poisson-disk sampling

Generate random samples on a mesh

Downsample a point cloud to have a blue noise distribution

Downsample a point cloud on a voxel grid

Compute closest points on a mesh

Estimating normals from a point cloud

Approximate Wasserstein (Sinkhorn) distance between two point clouds

Chamfer distance between two point clouds

Hausdorff distance between two point clouds

K-nearest-neighbors between two point clouds

Generating point samples in the square and cube with Lloyd relaxation

Compute shortest signed distances to a triangle mesh with fast winding numbers

Deduplicating Point Clouds and Meshes

Point Clouds:

Meshes:

Smoothing a Mesh

Making a Mesh Watertight

Comments

Releases(v0.28.1)

v0.28.1(Jan 4, 2023)

v0.28.0(Jan 1, 2023)

v0.27.0(Aug 19, 2022)

v0.25.1(Jul 24, 2022)

v0.25.0(Jul 24, 2022)

v0.24.2(Jul 24, 2022)

v0.24.1(Jul 20, 2022)

v0.24.0(Jul 7, 2022)

v0.23.1(May 10, 2022)

v0.22.0(May 1, 2022)

v0.21.11(Mar 11, 2022)

v0.21.8(Feb 16, 2022)

v0.21.7(Feb 16, 2022)

v0.21.4(Feb 3, 2022)

v0.21.3(Jan 31, 2022)

v0.21.2(Jan 26, 2022)

0.21.2(Jan 26, 2022)

0.19.0(Dec 13, 2021)

0.18.0(Jun 4, 2021)

0.17.2(Mar 11, 2021)

0.17.1(Mar 11, 2021)

0.16.0(Oct 14, 2020)

0.15.1(May 6, 2020)

0.15.0(May 5, 2020)

0.13.0(Feb 5, 2020)

0.12.2(Feb 4, 2020)

0.12.1(Feb 4, 2020)

0.12.0(Nov 21, 2019)

0.11.1(Nov 20, 2019)

0.11.0(Nov 18, 2019)

Owner

Francis Williams

The AugNet Python module contains functions for the fast computation of image similarity.

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