Point Cloud Color Constancy
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00362949" target="_blank" >RIV/68407700:21230/22:00362949 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/CVPR52688.2022.01913" target="_blank" >https://doi.org/10.1109/CVPR52688.2022.01913</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR52688.2022.01913" target="_blank" >10.1109/CVPR52688.2022.01913</a>
Alternative languages
Result language
angličtina
Original language name
Point Cloud Color Constancy
Original language description
In this paper, we present Point Cloud Color Constancy, in short PCCC, an illumination chromaticity estimation algorithm exploiting a point cloud. We leverage the depth information captured by the time-of-flight (ToF) sensor mounted rigidly with the RGB sensor, and form a 6D cloud where each point contains the coordinates and RGB intensities, noted as (x,y,z,r,g,b). PCCC applies the PointNet architecture to the color constancy problem, deriving the illumination vector point-wise and then making a global decision about the global illumination chromaticity. On two popular RGB-D datasets, which we extend with illumination information, as well as on a novel benchmark, PCCC obtains lower error than the state-of-the-art algorithms. Our method is simple and fast, requiring merely 16 x 16-size input and reaching speed over 140 fps (CPU time), including the cost of building the point cloud and net inference.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceeding 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
ISBN
978-1-6654-6946-3
ISSN
1063-6919
e-ISSN
2575-7075
Number of pages
10
Pages from-to
19718-19727
Publisher name
IEEE
Place of publication
Piscataway
Event location
New Orleans, Louisiana
Event date
Jun 19, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000870783005054