Related papers: Random Walks in Self-supervised Learning for Triangular Meshes

Random Walks in Self-supervised Learning for Triangular Meshes

URL: http://arxiv.org/abs/2503.00816v1
Date: Sun, 02 Mar 2025 09:45:06 GMT
Title: Random Walks in Self-supervised Learning for Triangular Meshes
Authors: Gal Yefet, Ayellet Tal,
Abstract summary: This study addresses the challenge of self-supervised learning for 3D mesh analysis.<n>It uses random walks as a form of data augmentation to generate diverse representations of mesh surfaces.<n>It employs a combination of contrastive and clustering losses.
Score: 11.19540223578237
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: This study addresses the challenge of self-supervised learning for 3D mesh analysis. It presents an new approach that uses random walks as a form of data augmentation to generate diverse representations of mesh surfaces. Furthermore, it employs a combination of contrastive and clustering losses. The contrastive learning framework maximizes similarity between augmented instances of the same mesh while minimizing similarity between different meshes. We integrate this with a clustering loss, enhancing class distinction across training epochs and mitigating training variance. Our model's effectiveness is evaluated using mean Average Precision (mAP) scores and a supervised SVM linear classifier on extracted features, demonstrating its potential for various downstream tasks such as object classification and shape retrieval.

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