CLOSED: A Dashboard for 3D Point Cloud Segmentation Analysis using Deep Learning

Thanasis Zoumpekas, Thanasis Zoumpekas, Guillem Molina, Anna Puig, Maria Salamó

2022

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.

Paper Citation

in Harvard Style

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 (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 403-410. DOI: 10.5220/0010826000003124

in Bibtex Style

@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 (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={403-410},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010826000003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

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