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English(EN) FILTR: Extracting Topological Features from Pretrained 3D Models

FILTR框架使用Transformer从3D模型中提取拓扑特征

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了FILTR，一个旨在从预训练的3D模型中提取拓扑特征的新型框架。该方法将Transformer解码器应用于生成持久性图（persistence diagrams），这些图直接从冻结的编码器中总结形状的多尺度结构。尽管现有的3D编码器显示出有限的全局拓扑信号，FILTR有效地利用其输出来近似这些图，从而实现从原始点云进行数据驱动的提取。 AI

影响 能够从3D点云中进行数据驱动的拓扑特征提取，可能改进计算机视觉中的形状分析和理解。

排序理由 这是一篇介绍从3D模型中提取拓扑特征新方法的学术论文。

在 arXiv cs.CV 阅读 →

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FILTR框架使用Transformer从3D模型中提取拓扑特征

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这是一篇介绍从3D模型中提取拓扑特征新方法的学术论文。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Louis Martinez, Maks Ovsjanikov ·

    FILTR: 从预训练3D模型中提取拓扑特征

    arXiv:2604.22334v1 Announce Type: new Abstract: Recent advances in pretraining 3D point cloud encoders (e.g., Point-BERT, Point-MAE) have produced powerful models, whose abilities are typically evaluated on geometric or semantic tasks. At the same time, topological descriptors ha…

  2. arXiv cs.CV TIER_1 English(EN) · Maks Ovsjanikov ·

    FILTR: 从预训练3D模型中提取拓扑特征

    Recent advances in pretraining 3D point cloud encoders (e.g., Point-BERT, Point-MAE) have produced powerful models, whose abilities are typically evaluated on geometric or semantic tasks. At the same time, topological descriptors have been shown to provide informative summaries o…

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