From 3D point-cloud data to explainable geometric deep learning: State-of-the-art and future challenges

^*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in Fachzeitschrift › Übersichtsarbeit

16 Quellenangaben (Web of Science)

Originalsprache	Englisch
Seitenumfang	45
Fachzeitschrift	Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Jahrgang	14
Ausgabenummer	6
Frühes Online-Datum	Sep. 2024
DOIs	https://doi.org/10.1002/widm.1554
Publikationsstatus	Veröffentlicht - Nov. 2024

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@article{d56151f434b9450b857019992bb23e97,

title = "From 3D point-cloud data to explainable geometric deep learning: State-of-the-art and future challenges",

author = "Anna Saranti and Bastian Pfeifer and C Gollob and K Stampfer and Andreas Holzinger",

year = "2024",

month = nov,

doi = "10.1002/widm.1554",

language = "English",

volume = "14",

journal = "Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery",

issn = "1942-4787",

publisher = "John Wiley \& Sons Inc.",

number = "6",

}

TY - JOUR

T1 - From 3D point-cloud data to explainable geometric deep learning: State-of-the-art and future challenges

AU - Pfeifer, Bastian

AU - Gollob, C

AU - Stampfer, K

A2 - Saranti, Anna

A2 - Holzinger, Andreas

PY - 2024/11

Y1 - 2024/11

UR - https://www.webofscience.com/wos/woscc/full-record/WOS:001314235200001

U2 - 10.1002/widm.1554

DO - 10.1002/widm.1554

M3 - Review

SN - 1942-4787

VL - 14

JO - Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

JF - Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

IS - 6

ER -