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Authors: Thanasis Zoumpekas 1 ; 2 ; Guillem Molina 1 ; Anna Puig 1 and Maria Salamó 1

Affiliations: 1 Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain ; 2 Unit Industrial Software Applications, RISC Software GmbH, Softwarepark 35, Hagenberg, Austria

Keyword(s): Segmentation, Point Clouds, Analysis, Dashboard, Data Visualization, Deep Learning.

Abstract: With the growing interest in 3D point cloud data, which is a set of data points in space used to describe a 3D object, and the inherent need to analyze it using deep neural networks, the visualization of data processes is critical for extracting meaningful insights. There is a gap in the literature for a full-suite visualization tool to analyse 3D deep learning segmentation models on point cloud data. This paper proposes such a tool to cover this gap, entitled point CLOud SEgmentation Dashboard (CLOSED). Specifically, we concentrate our efforts on 3D point cloud part segmentation, where the entire shape and the parts of a 3D object are significant. Our approach manages to (i) exhibit the learning evolution of neural networks, (ii) compare and evaluate different neural networks, (iii) highlight key-points of the segmentation process. We illustrate our proposal by analysing five neural networks utilizing the ShapeNet-part dataset.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Zoumpekas, T.; Molina, G.; Puig, A. and Salamó, M. (2022). CLOSED: A Dashboard for 3D Point Cloud Segmentation Analysis using Deep Learning. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-555-5; ISSN 2184-4321, pages 403-410. DOI: 10.5220/0010826000003124

@conference{visapp22,
author={Thanasis Zoumpekas. and Guillem Molina. and Anna Puig. and Maria Salamó.},
title={CLOSED: A Dashboard for 3D Point Cloud Segmentation Analysis using Deep Learning},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2022},
pages={403-410},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010826000003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - CLOSED: A Dashboard for 3D Point Cloud Segmentation Analysis using Deep Learning
SN - 978-989-758-555-5
IS - 2184-4321
AU - Zoumpekas, T.
AU - Molina, G.
AU - Puig, A.
AU - Salamó, M.
PY - 2022
SP - 403
EP - 410
DO - 10.5220/0010826000003124