Potree Renders Large Point Clouds in a Browser

Color coded image of Grand Canyon from Potree

Potree render of Grand Canyon

Thanks to Matt O’Banion at West Point and Mike Olsen at OSU for the heads up on Potree – a free, open-source, WebGL based point cloud renderer for large point clouds, developed at the Institute of Computer Graphics and Algorithms, TU Wien University in Vienna, Austria.

Potree was developed by Markus Schuetz for his Thesis in visual computing. That is a program that I wish more people had the opportunity to get involved with as it looks like the Institute at TU Wien is a leader in visualization, rendering and modeling.

Examples of the power of Potree can be seen in the examples at the Potree.org website, or Matt O’Banion provide a link to a project he worked on with Mike Olsen in New Zealand after one of the devastating earthquakes.

Data files must first be processed with a convertor. The current version, PotreeConvertor 1.6 can be downloaded here for free. You will also need to set up a web server to host the indexed files, but then anyone can view the 3D point cloud using just their browser. They do not have to download any software.

Matt recommends using Chrome or Firefox. Internet Explorer and Edge seem to have problems.

UPDATE: I received a message from Riegl informing me that they have a Potree project website. Click here to visit.

Be careful when you step on the gas. This is not a Ferrari, it’s a McLaren.

For more information on the programs at TU Wien click here.

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This entry was posted in 3D Modeling, Data, Software, Visualization and tagged , , , , . Bookmark the permalink.

1 Response to Potree Renders Large Point Clouds in a Browser

  1. Most of the 3DEP data is available in Potree at https://usgs.entwine.io/ as an Amazon Public Dataset. Read more about it at https://www.usgs.gov/news/usgs-3dep-lidar-point-cloud-now-available-amazon-public-dataset

    The Entwine open source software at https://entwine.io can be used to process data for access via Potree, and it is available for OSX, Windows, and Linux via Conda.

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