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Computer Graphics Forum
Article . 2023 . Peer-reviewed
License: CC BY
Data sources: Crossref
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https://dx.doi.org/10.48550/ar...
Article . 2023
License: CC BY NC ND
Data sources: Datacite
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Article . 2024
Data sources: DBLP
DBLP
Preprint . 2023
Data sources: DBLP
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GPU‐Accelerated LOD Generation for Point Clouds

Authors: Markus Schütz; Bernhard Kerbl; Philip Klaus; Michael Wimmer 0001;

GPU‐Accelerated LOD Generation for Point Clouds

Abstract

AbstractAbout: We introduce a GPU‐accelerated LOD construction process that creates a hybrid voxel‐point‐based variation of the widely used layered point cloud (LPC) structure for LOD rendering and streaming. The massive performance improvements provided by the GPU allow us to improve the quality of lower LODs via color filtering while still increasing construction speed compared to the non‐filtered, CPU‐based state of the art.Background: LOD structures are required to render hundreds of millions to trillions of points, but constructing them takes time.Results: LOD structures suitable for rendering and streaming are constructed at rates of about 1 billion points per second (with color filtering) to 4 billion points per second (sample‐picking/random sampling, state of the art) on an RTX 3090 – an improvement of a factor of 80 to 400 times over the CPU‐based state of the art (12 million points per second). Due to being in‐core, model sizes are limited to about 500 million points per 24GB memory.Discussion: Our method currently focuses on maximizing in‐core construction speed on the GPU. Issues such as out‐of‐core construction of arbitrarily large data sets are not addressed, but we expect it to be suitable as a component of bottom‐up out‐of‐core LOD construction schemes.

Keywords

FOS: Computer and information sciences, Computer Science - Graphics, Graphics (cs.GR)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
10
Top 10%
Top 10%
Top 10%
Green
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