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Authors: Ahmed Abouelazm ; Igor Vozniak ; Nils Lipp ; Pavel Astreika and Christian Mueller

Affiliation: Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Saarbruecken, Germany

Keyword(s): Point Clouds, 3D Deep Learning, Distance Metric Learning, Similarity Preserving Embedding.

Abstract: Point cloud processing and 3D model retrieval methods have received a lot of interest as a result of the recent advancement in deep learning, computing hardware, and a wide range of available 3D sensors. Many state-of-the-art approaches utilize distance metric learning for solving the 3D model retrieval problem. However, the majority of these approaches disregard the variation in shape and properties of instances belonging to the same class known as intra-class variance, and focus on semantic labels as a measure of relevance. In this work, we present two novel loss functions for similarity-preserving point cloud embedding, in which the distance between point clouds in the embedding space is directly proportional to the ground truth distance between them using a similarity or distance measure. The building block of both loss functions is the forward passing of n-pair input point clouds through a Siamese network. We utilize ModelNet 10 dataset in the course of numerical evaluations und er classification and mean average precision evaluation metrics. The reported quantitative and qualitative results demonstrate enhancement in retrieved models both quantitatively and qualitatively by a significant margin. (More)

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Paper citation in several formats:
Abouelazm, A.; Vozniak, I.; Lipp, N.; Astreika, P. and Mueller, C. (2023). Deep Distance Metric Learning for Similarity Preserving Embedding of Point Clouds. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 570-581. DOI: 10.5220/0011627100003417

@conference{visapp23,
author={Ahmed Abouelazm. and Igor Vozniak. and Nils Lipp. and Pavel Astreika. and Christian Mueller.},
title={Deep Distance Metric Learning for Similarity Preserving Embedding of Point Clouds},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={570-581},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011627100003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Deep Distance Metric Learning for Similarity Preserving Embedding of Point Clouds
SN - 978-989-758-634-7
IS - 2184-4321
AU - Abouelazm, A.
AU - Vozniak, I.
AU - Lipp, N.
AU - Astreika, P.
AU - Mueller, C.
PY - 2023
SP - 570
EP - 581
DO - 10.5220/0011627100003417
PB - SciTePress

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