Diploma and Master Theses (authored and supervised):
"Potree: Rendering Large Point Clouds in Web Browsers";
Supervisor: M. Wimmer;
final examination: 2016-09-19.
This thesis introduces Potree, a web-based renderer for large point clouds. It allows users
to view data sets with billions of points, from sources such as LIDAR or photogrammetry,
in real time in standard web browsers.
One of the main advantages of point cloud visualization in web browser is that it
allows users to share their data sets with clients or the public without the need to install
third-party applications and transfer huge amounts of data in advance. The focus on
large point clouds, and a variety of measuring tools, also allows users to use Potree to
look at, analyze and validate raw point cloud data, without the need for a time-intensive
and potentially costly meshing step.
The streaming and rendering of billions of points in web browsers, without the need
to load large amounts of data in advance, is achieved with a hierarchical structure that
stores subsamples of the original data at different resolutions. A low resolution is stored
in the root node and with each level, the resolution gradually increases. The structure
allows Potree to cull regions of the point cloud that are outside the view frustum, and
to render distant regions at a lower level of detail.
The result is an open source point cloud viewer, which was able to render point cloud
data sets of up to 597 billion points, roughly 1.6 terabytes after compression, in real time
in a web browser.
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.