pointcloudset

Analyze large datasets of point clouds recorded over time in an efficient way.

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Code | Documentation

.. _Code: https://github.com/virtual-vehicle/pointcloudset .. _Documentation: https://virtual-vehicle.github.io/pointcloudset/

Features ################################################

• Handles point clouds over time
• Building complex pipelines with a clean and maintainable code

.. code-block:: python

newpointcloud = pointcloud.limit("x",-5,5).filter("quantile","reflectivity", ">",0.5)

• Apply arbitrary functions to datasets of point clouds

.. code-block:: python

def isolate_target(frame: PointCloud) -> PointCloud: return frame.limit("x",0,1).limit("y",0,1)

def diff_to_pointcloud(pointcloud: PointCloud, to_compare: PointCloud) -> PointCloud: return pointcloud.diff("pointcloud", to_compare)

result = dataset.apply(isolate_target).apply(diff_to_pointcloud, to_compare=dataset[0])

• Includes powerful aggregation method agg similar to pandas

.. code-block:: python

dataset.agg(["min","max","mean","std"])

• Support for large files with lazy evaluation and parallel processing

.. image:: https://raw.githubusercontent.com/virtual-vehicle/pointcloudset/master/images/dask.gif :width: 600

• Support for numerical data per point (intensity, range, noise …)
• Interactive 3D visualisation

.. image:: https://raw.githubusercontent.com/virtual-vehicle/pointcloudset/master/images/tree.gif :width: 600

• High level processing based on dask, pandas, open3D and pyntcloud
• Docker image is available
• Optimised - but not limited to - automotive lidar
• Directly read ROS files and many pointcloud file formats
• A command line tool to convert ROS 1 & 2 files

Use case examples ################################################

• Post processing and analytics of a lidar dataset recorded by ROS
• A collection of multiple lidar scans from a terrestrial laser scanner
• Comparison of multiple point clouds to a ground truth
• Analytics of point clouds over time
• Developing algorithms on a single frame and then applying them to huge datasets

Installation with pip ################################################

Install python package with pip:

.. code-block:: console

pip install pointcloudset

Installation with Docker ################################################

The easiest way to get started is to use the pre-build docker tgoelles/pointcloudset_ or use tgoelles/pointcloudset_base_ to get a container with all dependencies and install pointcloudset there.

.. _tgoelles/pointcloudset_base: https://hub.docker.com/repository/docker/tgoelles/pointcloudset_base .. _tgoelles/pointcloudset: https://hub.docker.com/repository/docker/tgoelles/pointcloudset

Quickstart ################################################

.. code-block:: python

from pointcloudset import Dataset, PointCloud from pathlib import Path import urllib.request

urllib.request.urlretrieve("https://github.com/virtual-vehicle/pointcloudset/raw/master/tests/testdata/test.bag", "test.bag") urllib.request.urlretrieve("https://github.com/virtual-vehicle/pointcloudset/raw/master/tests/testdata/las_files/test_tree.las", "test_tree.las")

dataset = Dataset.from_file(Path("test.bag"), topic="/os1_cloud_node/points", keep_zeros=False) pointcloud = dataset[1] tree = PointCloud.from_file(Path("test_tree.las"))

tree.plot("x", hover_data=True)

This produces the plot from the animation above.

• Read the html documentation_.
• Have a look at the tutorial notebooks_ in the documentation folder
• For even more usage examples you can have a look at the tests

.. _html documentation: https://virtual-vehicle.github.io/pointcloudset/ .. _tutorial notebooks: https://github.com/virtual-vehicle/pointcloudset/tree/master/doc/sphinx/source/tutorial_notebooks

CLI to convert ROS1 and ROS2 files: pointcloudset convert ##########################################################

The package includes a powerful CLI to convert pointclouds in ROS1 & 2 files into many formats like pointcloudset, csv, las and many more. It is capable of handling both mcap and db3 ROS files.

.. code-block:: console

pointcloudset convert --output-format csv --output-dir converted_csv test.bag

.. image:: https://raw.githubusercontent.com/virtual-vehicle/pointcloudset/master/images/cli_demo.gif :width: 600

You can view PointCloud2 messages with

.. code-block:: console

pointcloudset topics test.bag

Comparison to related packages ################################################

. ROS <http://wiki.ros.org/rosbag/Code%20API>_ - bagfiles can contain many point clouds from different sensors.

The downside of the format is that it is only suitable for serial access and not well suited for data analytics and post processing.

. pyntcloud <https://github.com/daavoo/pyntcloud>_ - Only for single point clouds. This package is used as the basis for the

PointCloud object.

. open3d <https://github.com/intel-isl/Open3D>_ - Only for single point clouds. Excellent package, which is used for some

methods on the PointCloud.

. pdal <https://github.com/PDAL/PDAL>_ - Works also with pipelines on point clouds but is mostly focused on single point cloud processing.

Pointcloudset is purely in python and based on pandas DataFrames. In addition pointcloudset works in parallel to process large datasets.

Citation and contact ################################################

.. |orcid| image:: https://orcid.org/sites/default/files/images/orcid_16x16.png :target: https://orcid.org/0000-0002-3925-6260>

|orcid| Thomas Gölles <https://orcid.org/0000-0002-3925-6260>_ email: thomas.goelles@v2c2.at

Please cite our JOSS paper_ if you use pointcloudset.

.. _JOSS paper: https://joss.theoj.org/papers/10.21105/joss.03471#

.. code-block:: bib

@article{Goelles2021, doi = {10.21105/joss.03471}, url = {https://doi.org/10.21105/joss.03471}, year = {2021}, publisher = {The Open Journal}, volume = {6}, number = {65}, pages = {3471}, author = {Thomas Goelles and Birgit Schlager and Stefan Muckenhuber and Sarah Haas and Tobias Hammer}, title = {pointcloudset: Efficient Analysis of Large Datasets of Point Clouds Recorded Over Time}, journal = {Journal of Open Source Software} }