An Easy Way to Work and Visualize Lidar Data in Python

Ingest, process, and Visualize 3D Point Cloud Data in Python

Photo by Adam Jícha on Unsplash

Working with 3D point cloud data is challenging. The collection of the points in one Lidar image is usually extensive and might reach millions of points depending on the image size. Handling the height dimension also poses other complexities.

A point cloud is a set of data points in space. The points may represent a 3D shape or object. Each point position has its set of Cartesian coordinates (X, Y, Z).

However, we have different tools that make working with Lidar data more straightforward than ever before. This blog post will teach you an easy way to ingest, manipulate, and visualize Lidar data in Python.

Reading & Accessing Lidar Data

In this tutorial, we use Laspy, a Python library for lidar LAS/LAZ IO, to ingest the point cloud data. Later, we will use open3D, a modern library for 3D data processing, to visualize the 3D lidar data. So let us import these libraries first.

import laspy
import open3d as o3d
import numpy as np

Let us read the lidar data with Laspy. Reading is done using laspy.read() function. You can also use laspy.open() if you only want the metadata but not the…