Information
- Publication Type: PhD-Thesis
- Workgroup(s)/Project(s):
- Date: April 2021
- Open Access: yes
- 1st Reviewer: Mario Botsch
- 2nd Reviewer: Carsten Dachsbacher
- Rigorosum: 7. April 2021
- First Supervisor: Michael Wimmer
- Pages: 107
- Keywords: point cloud rendering, lidar
Abstract
Laser scanning, photogrammetry and other 3D scanning approaches generate data sets comprising millions to trillions of points. Modern GPUs can easily render a few million and up to tens of millions of points in real time, but data sets with hundreds of millions of points and more require acceleration structures to be rendered in real time. In this thesis, we present three contributions to the state of the art with the goal of improving the performance as well as the quality of real-time rendered point clouds.Two of our contributions address the performance of LOD structure generation. State-of-the-art approaches achieve a throughput of up to around 1 million points per second, which requires users to wait minutes even for smaller data sets with a few hundred million points. Our proposed solutions are: A bottom-up LOD generation approach that creates LOD structures up to an order of magnitude faster than previous work, and a progressive rendering approach that is capable of rendering any point cloud that fits in GPU memory in real time, without the need to generate LOD structures at all. The former achieves a throughput of up to 10 million points per second, and the latter is capable of loading point clouds at rates of up to 37 million points per second from an industry-standard point-cloud format (LAS), and up to 100 million points per second if the file format matches the vertex buffer format. Since it does not need LOD structures, the progressive rendering approach can render already loaded points right away while additional points are still being loaded.
Our third contribution improves the quality of LOD-based point-cloud rendering by introducing a continuous level-of-detail approach that produces gradual transitions in point density, rather than the characteristic and noticeable blocks from discrete LOD structures. It is mainly targeted towards VR applications, where discrete levels of detail are especially noticeable and disturbing, in a large part due to the popping of chunks of points during motion.
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BibTeX
@phdthesis{SCHUETZ-2021-DISS,
title = "Interactive Exploration of Point Clouds",
author = "Markus Sch\"{u}tz",
year = "2021",
abstract = "Laser scanning, photogrammetry and other 3D scanning
approaches generate data sets comprising millions to
trillions of points. Modern GPUs can easily render a few
million and up to tens of millions of points in real time,
but data sets with hundreds of millions of points and more
require acceleration structures to be rendered in real time.
In this thesis, we present three contributions to the state
of the art with the goal of improving the performance as
well as the quality of real-time rendered point clouds. Two
of our contributions address the performance of LOD
structure generation. State-of-the-art approaches achieve a
throughput of up to around 1 million points per second,
which requires users to wait minutes even for smaller data
sets with a few hundred million points. Our proposed
solutions are: A bottom-up LOD generation approach that
creates LOD structures up to an order of magnitude faster
than previous work, and a progressive rendering approach
that is capable of rendering any point cloud that fits in
GPU memory in real time, without the need to generate LOD
structures at all. The former achieves a throughput of up to
10 million points per second, and the latter is capable of
loading point clouds at rates of up to 37 million points per
second from an industry-standard point-cloud format (LAS),
and up to 100 million points per second if the file format
matches the vertex buffer format. Since it does not need LOD
structures, the progressive rendering approach can render
already loaded points right away while additional points are
still being loaded. Our third contribution improves the
quality of LOD-based point-cloud rendering by introducing a
continuous level-of-detail approach that produces gradual
transitions in point density, rather than the characteristic
and noticeable blocks from discrete LOD structures. It is
mainly targeted towards VR applications, where discrete
levels of detail are especially noticeable and disturbing,
in a large part due to the popping of chunks of points
during motion. ",
month = apr,
pages = "107",
address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
school = "Research Unit of Computer Graphics, Institute of Visual
Computing and Human-Centered Technology, Faculty of
Informatics, TU Wien ",
keywords = "point cloud rendering, lidar",
URL = "https://www.cg.tuwien.ac.at/research/publications/2021/SCHUETZ-2021-DISS/",
}