3DOR 2024 - Short Papers

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

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    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.
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    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.
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    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.