3DOR 2024 - Short Papers

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/3607033

August 26 - 27, 2024 | Santiago, Chile

Table of Contents
Short Papers
SHREC2024: Non-rigid Complementary Shapes Retrieval in Protein-protein Interactions
[full paper Image] [meta data Image]
Taher Yacoub, Nika Zarubina, Camille Depenveiller, Hoang-Phuc Nguyen, Vinh-Toan Vong, Minh-Triet Tran, Yuki Kagaya, Tsukasa Nakamura, Daisuke Kihara, Florent Langenfeld, and Matthieu Montes
Self-distillation for Efficient Object-level Point Cloud Learning
[full paper Image] [meta data Image]
Lucas Oyarzún, Ivan Sipiran, and José M. Saavedra

BibTeX (3DOR 2024 - Short Papers)
@inproceedings{
10.2312:3dor.20242019,

booktitle = {
Eurographics Workshop on 3D Object Retrieval},

editor = {
Biasotti, Silvia
 and 
Bustos, Benjamin
 and 
Schreck, Tobias
 and 
Sipiran, Ivan
 and 
Veltkamp, Remco C.
},
title = {{
Eurographics Workshop on 3D Object Retrieval - Short Papers: Frontmatter}},

author = {
Biasotti, Silvia
 and 
Bustos, Benjamin
 and 
Schreck, Tobias
 and 
Sipiran, Ivan
 and 
Veltkamp, Remco C.
},
year = {
2024},

publisher = {
The Eurographics Association},


ISSN = {1997-0471},
ISBN = {978-3-03868-242-4},

DOI = {
10.2312/3dor.20242019}

}
@inproceedings{
10.2312:3dor.20241173,

booktitle = {
Eurographics Workshop on 3D Object Retrieval},

editor = {
Biasotti, Silvia
 and 
Bustos, Benjamin
 and 
Schreck, Tobias
 and 
Sipiran, Ivan
 and 
Veltkamp, Remco C.
},
title = {{
SHREC2024: Non-rigid Complementary Shapes Retrieval in Protein-protein Interactions}},

author = {
Yacoub, Taher
 and 
Zarubina, Nika
 and 
Montes, Matthieu
 and 
Depenveiller, Camille
 and 
Nguyen, Hoang-Phuc
 and 
Vong, Vinh-Toan
 and 
Tran, Minh-Triet
 and 
Kagaya, Yuki
 and 
Nakamura, Tsukasa
 and 
Kihara, Daisuke
 and 
Langenfeld, Florent
},
year = {
2024},

publisher = {
The Eurographics Association},


ISSN = {1997-0471},
ISBN = {978-3-03868-242-4},

DOI = {
10.2312/3dor.20241173}

}
@inproceedings{
10.2312:3dor.20241174,

booktitle = {
Eurographics Workshop on 3D Object Retrieval},

editor = {
Biasotti, Silvia
 and 
Bustos, Benjamin
 and 
Schreck, Tobias
 and 
Sipiran, Ivan
 and 
Veltkamp, Remco C.
},
title = {{
Self-distillation for Efficient Object-level Point Cloud Learning}},

author = {
Oyarzún, Lucas
 and 
Sipiran, Ivan
 and 
Saavedra, José M.
},
year = {
2024},

publisher = {
The Eurographics Association},


ISSN = {1997-0471},
ISBN = {978-3-03868-242-4},

DOI = {
10.2312/3dor.20241174}

}

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Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Eurographics Workshop on 3D Object Retrieval - Short Papers: Frontmatter
    (The Eurographics Association, 2024) Biasotti, Silvia; Bustos, Benjamin; Schreck, Tobias; Sipiran, Ivan; Veltkamp, Remco C.; Biasotti, Silvia; Bustos, Benjamin; Schreck, Tobias; Sipiran, Ivan; Veltkamp, Remco C.
  • No Thumbnail Available
    Item
    SHREC2024: Non-rigid Complementary Shapes Retrieval in Protein-protein Interactions
    (The Eurographics Association, 2024) Yacoub, Taher; Zarubina, Nika; Depenveiller, Camille; Nguyen, Hoang-Phuc; Vong, Vinh-Toan; Tran, Minh-Triet; Kagaya, Yuki; Nakamura, Tsukasa; Kihara, Daisuke; Langenfeld, Florent; Montes, Matthieu; Biasotti, Silvia; Bustos, Benjamin; Schreck, Tobias; Sipiran, Ivan; Veltkamp, Remco C.
    The aim of this SHREC 2024 track is to compare different algorithms for retrieving non-rigid complementary shape pairs, applied in the context of 3D objects being more complex (e.g. with many folds and roughness) such as proteins. The dataset used for this benchmark is based on 52 selected protein-protein complexes for which an experimental structure is publicly available. One of the main difficulties of this challenge is the non-inclusion of the shapes derived from the ground truth conformations in the dataset. Different metrics were used to evaluate the retrieval performance (nearest-neighbor, first-tier, second-tier, and true positives) and to evaluate the quality of the predicted poses (TM-score, lDDT, ICS, IPS and DockQ - those metrics are classically used in the Critical Assessment of PRediction of Interactions challenges). Two teams took part in this challenge and were able to return the expected results. This paper discusses these results and prospects of retrieval methods based only on the protein shape information in the absence of atomic data, in a large context of protein-protein docking.
  • No Thumbnail Available
    Item
    Self-distillation for Efficient Object-level Point Cloud Learning
    (The Eurographics Association, 2024) Oyarzún, Lucas; Sipiran, Ivan; Saavedra, José M.; Biasotti, Silvia; Bustos, Benjamin; Schreck, Tobias; Sipiran, Ivan; Veltkamp, Remco C.
    The emerging accessibility of 3D point cloud data has catalyzed the evolution of deep-learning methodologies for analysis and processing of 3D data. However, the efficacy of neural networks in this domain is often inhibited by the necessity for extensively labelled datasets. In this study, we investigate the application of self-distillation techniques based on Siamese networks, BYOL and SIMSIAM, to pre-train encoders designed for 3D point cloud processing. These pre-training regimes enable encoders to generate data representations without label reliance, potentially supporting network performance in downstream tasks. The efficacy of these learned representations was assessed using the established evaluation methodologies for pre-training: linear probing and finetuning. We also incorporate an analysis of self-supervised features in a retrieval scenario. Furthermore, the impact of these representations on subsequent applications was evaluated via transfer learning by employing pre-trained models as a foundation for standard test datasets.