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English(EN) MeshLoom: Feed-Forward Non-Rigid Registration of Mesh Sequences

MeshLoom网络推进非刚性网格配准

研究人员推出MeshLoom,一种专为非刚性网格序列设计的新型前馈配准网络。该方法通过直接重建顶点变形,绕过了传统方法如昂贵的每实例优化和受限的对象类别等限制。MeshLoom效率高,可在几秒钟内处理多个网格,并利用拓扑感知的编码器-解码器架构,融合了锚点网格拓扑与特定帧的线索,如形状潜在表示和图像特征。该网络在非刚性配准方面取得了最先进的成果,也可应用于运动插值和网格变形。 AI

影响 引入了一种新颖的网络架构,用于高效的非刚性网格配准,可能改进计算机视觉和图形学中的应用。

排序理由 该集群包含一篇在arXiv上发表的研究论文,详细介绍了非刚性网格序列配准的新方法。

在 arXiv cs.CV 阅读 →

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Jianqi Chen, Jiraphon Yenphraphai, Xiangjun Tang, Sergey Tulyakov, Chaoyang Wang, Peter Wonka, Rameen Abdal ·

    MeshLoom: Feed-Forward Non-Rigid Registration of Mesh Sequences

    arXiv:2606.17027v1 Announce Type: new Abstract: We present MeshLoom, a feed-forward registration network that directly reconstructs vertex deformations across mesh sequences. Our approach advances non-rigid registration beyond existing models, which are typically constrained by c…

  2. arXiv cs.CV TIER_1 English(EN) · Rameen Abdal ·

    MeshLoom: Feed-Forward Non-Rigid Registration of Mesh Sequences

    We present MeshLoom, a feed-forward registration network that directly reconstructs vertex deformations across mesh sequences. Our approach advances non-rigid registration beyond existing models, which are typically constrained by costly per-instance optimization, narrow object c…