Survey and Evaluation of Neural 3D Shape Classification Approaches
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10431676" target="_blank" >RIV/00216208:11320/21:10431676 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=VgBcbVUd1e" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=VgBcbVUd1e</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TPAMI.2021.3102676" target="_blank" >10.1109/TPAMI.2021.3102676</a>
Alternative languages
Result language
angličtina
Original language name
Survey and Evaluation of Neural 3D Shape Classification Approaches
Original language description
Classification of 3D objects - the selection of a category in which each object belongs - is of great interest in the field of machine learning. Numerous researchers use deep neural networks to address this problem, altering the network architecture and representation of the 3D shape used as an input. To investigate the effectiveness of their approaches, we conduct an extensive survey of existing methods and identify common ideas by which we categorize them into a taxonomy. Second, we evaluate 11 selected classification networks on two 3D object datasets, extending the evaluation to a larger dataset on which most of the selected approaches have not been tested yet. For this, we provide a framework for converting shapes from common 3D mesh formats into formats native to each network, and for training and evaluating different classification approaches on this data. Despite being partially unable to reach the accuracies reported in the original papers, we compare the relative performance of the approaches as well as their performance when changing datasets as the only variable to provide valuable insights into performance on different kinds of data. We make our code available to simplify running training experiments with multiple neural networks with different prerequisites.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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/EF19_073%2F0016935" target="_blank" >EF19_073/0016935: Grant schemes at Charles University</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE [online]
ISSN
1939-3539
e-ISSN
—
Volume of the periodical
44
Issue of the periodical within the volume
11
Country of publishing house
US - UNITED STATES
Number of pages
22
Pages from-to
8635-8656
UT code for WoS article
000864325900094
EID of the result in the Scopus database
2-s2.0-85113242600