ICTree: Automatic Perceptual Metrics for Tree Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU139209" target="_blank" >RIV/00216305:26230/21:PU139209 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/21:00355963

Výsledek na webu

<a href="https://doi.org/10.1145/3478513.3480519" target="_blank" >https://doi.org/10.1145/3478513.3480519</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3478513.3480519" target="_blank" >10.1145/3478513.3480519</a>

Jazyk výsledku

angličtina

Název v původním jazyce

ICTree: Automatic Perceptual Metrics for Tree Models

Popis výsledku v původním jazyce

Many algorithms for synthetic tree generation exist, but the visual quality of the generated models is unknown. This problem is usually solved by performing limited user studies or by side-by-side comparison. We introduce an automated system for quality assessment of the tree model based on their perception. We conducted a user study in which over one million pairs of images were compared to collect subjective perceptual scores of generated trees. The perceptual score was used to train two neural-network-based predictors. A view independent ICTreeF uses the tree models geometric features that are easy to extract from any model. The second is ICTreeI that estimates the perceived visual quality of a tree from its image. Moreover, to provide an insight into the problem, we deduce intrinsic attributes and evaluate which features make trees look like real trees. In particular, we show that branching angles, length of branches, and widths are critical for perceived realism.

Název v anglickém jazyce

ICTree: Automatic Perceptual Metrics for Tree Models

Popis výsledku anglicky

Many algorithms for synthetic tree generation exist, but the visual quality of the generated models is unknown. This problem is usually solved by performing limited user studies or by side-by-side comparison. We introduce an automated system for quality assessment of the tree model based on their perception. We conducted a user study in which over one million pairs of images were compared to collect subjective perceptual scores of generated trees. The perceptual score was used to train two neural-network-based predictors. A view independent ICTreeF uses the tree models geometric features that are easy to extract from any model. The second is ICTreeI that estimates the perceived visual quality of a tree from its image. Moreover, to provide an insight into the problem, we deduce intrinsic attributes and evaluate which features make trees look like real trees. In particular, we show that branching angles, length of branches, and widths are critical for perceived realism.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

ACM TRANSACTIONS ON GRAPHICS

ISSN

0730-0301

e-ISSN

1557-7368

Svazek periodika

40

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

1-15

Kód UT WoS článku

000729846700035

EID výsledku v databázi Scopus

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