Welcome to pointcloudset’s documentation!¶

Features¶

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

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

Apply arbitrary functions to datasets of point clouds

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

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

Support for large files with lazy evaluation and parallel processing

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

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

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

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:

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.

Quickstart¶

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

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.

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

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

You can view PointCloud2 messages with

pointcloudset topics test.bag

Citation and contact¶

Thomas Gölles email: thomas.goelles@v2c2.at

Please cite our JOSS paper if you use pointcloudset.

@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}
}

Tutorial

Convert ROS 1 and ROS 2 files

pointcloudset CLI

Contribute

Python API