用随机游走增强3D网格自监督学习，提升分类与检索性能
Random Walks in Self-supervised Learning for Triangular Meshes
- 通过随机游走生成网格表面多样化表征
- 结合对比与聚类损失，提升特征区分度与训练稳定性
- 适合3D形状分析、对象分类等下游任务研究者
本研究针对3D网格分析中的自监督学习挑战，提出一种新方法：利用随机游走作为数据增强手段，生成网格表面的多样表征。同时结合对比损失与聚类损失，对比学习框架使同一网格的不同增强实例相似度最大化，不同网格间相似度最小化；聚类损失则在训练过程中增强类别区分性并降低训练方差。模型性能通过mAP得分及基于提取特征的监督SVM线性分类器评估，验证了其在对象分类、形状检索等下游任务中的潜力。
原文摘要 · Abstract (English)
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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