As mentioned in the introduction, 3D point clouds could be obtained from many different sources, each one with its own file format.

In addition to file formats used by each manufacturer, point clouds may also be stored in generic binary and ascii formats using different programming languages.

PyntCloud provides reading and writing routines for many common 3D file and generic array formats (more formats will be added in the near future):


classmethod PyntCloud.from_file(filename, **kwargs)[source]

Extract data from file and construct a PyntCloud with it.

  • filename (str) – Path to the file from which the data will be read
  • kwargs (only usable in some formats) –

PyntCloud – PyntCloud instance, containing all valid elements in the file.

Return type:


from pyntcloud import PyntCloud
my_point_cloud = PyntCloud.from_file("some_file.ply")


PyntCloud.to_file(filename, also_save=None, **kwargs)[source]

Save PyntCloud data to file.

  • filename (str) – Path to the file from which the data will be read
  • also_save (list of str, optional) – Default: None Names of the attributes that will be extracted from the PyntCloud to be saved in addition to points. Usually also_save=[“mesh”]
  • kwargs (only usable in some formats) –
# my_point_cloud is a PyntCloud instance
my_point_cloud.to_file("out_file.obj", internal=["points", "mesh"])

Alternative ways for creating PyntClouds

Even though PyntCloud includes readers for some of the most common 3D file formats, there are many other formats and sources that you can use to store point cloud data.

That’s why although PyntCloud will include support for other file formats, it will never cover all.

The good news is that as long as you can load the data into Python, you can create a PyntCloud instance manually.

The key thing to understand is that you can’t just plug in the raw data into the PyntCloud constructor; there are some restrictions.

These restrictions are covered in Points.

As long as you can adapt your data to these restrictions, you will be able to construct a PyntCloud from formats that are not covered in from_file.

Note about ASCII files

There are many formats englobed in these kinds of files: .asc, .txt, .pts, …

Normally, the first 3 columns represent the X,Y,Z coordinates of the point and the rest of the columns represent some scalar field associated to that point (Maybe R,G,B values, or Nx,Ny,Nz, etc). But there is no official format specification.

Given all the possibilities that this brings, PyntCloud.from_file accepts keyword arguments in order to let the user adjust the loading for every possibility.

Internally, PyntCloud.from_file is just calling the pandas function .read_csv when you give it a valid ascii format.

So check the linked documentation to explore all the possible arguments in order to adjust them to read your ascii file.

For example, given a example.pts file with this content:

-0.037829 0.12794 0.004474
-0.044779 0.128887 0.001904
-0.068009 0.151244 0.037195
-0.002287 0.13015 0.02322
-0.022605 0.126675 0.007155
-0.025107 0.125921 0.006242
-0.03712 0.127449 0.001795
0.033213 0.112692 0.027686

You can construct a PyntCloud as follows:

import pandas as pd
from pyntcloud import PyntCloud

cloud = PyntCloud.from_file("example.pts",
                            sep=" ",