pointcloudset

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

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])

Support for large files with lazy evaluation and parallel processing

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

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

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

Comparison to related packages

ROS - 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 - Only for single point clouds. This package is used as the basis for the PointCloud object.
open3d - Only for single point clouds. Excellent package, which is used for some methods on the PointCloud.
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

Thomas Gölles email: thomas.goelles@v2c2.at

ADD link to JOSS paper here and DOI

hechth/pointcloudset