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English(EN) CGGT: Curve-Grounded Geometry Transformer for 3D Parametric Curve Reconstruction

新型Transformer模型从稀疏图像重建3D曲线

研究人员开发了CGGT(Curve-Grounded Geometry Transformer),一种旨在从稀疏、未姿态的多视图图像中重建可编辑的3D参数化曲线的模型。该新方法绕过了现有NeRF和3DGS方法中常见的密集相机视图或昂贵的每场景优化需求。CGGT集成了用于特征学习的Transformer编码器和用于实例关联的掩码注意力解码器,使其能够单次预测相机参数、深度图和曲线掩码。该系统在Wireframe-100K(一个包含10万个CAD模型的庞大数据集)上进行训练,并展示了对真实世界图像的强大泛化能力,能有效区分结构边缘和视点相关的边缘。 AI

影响 这项研究推进了从有限视觉数据进行3D重建的技术,有望改进CAD建模和计算机图形学应用。

排序理由 该集群描述了一篇关于3D曲线重建新方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新型Transformer模型从稀疏图像重建3D曲线

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该集群描述了一篇关于3D曲线重建新方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Zhirui Gao, Renjiao Yi, Yunfan Ye, Ruizhen Hu, Chenyang Zhu, Wei Chen, Kai Xu ·

    CGGT:用于3D参数化曲线重建的曲线接地几何Transformer

    arXiv:2609.14521v1 Announce Type: new Abstract: Recovering editable 3D parametric curves from 2D images is a fundamental challenge in computer graphics, bridging pixel-based perception and vector-based CAD modeling. Existing NeRF- and 3DGS-based methods often rely on dense calibr…