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English(EN) MvFFN: Multi-view Floor plan Feed-forward Network for Unposed Wide-Baseline Panorama Layout Reconstruction

新的MvFFN方法从全景图像重建楼层平面

研究人员开发了多视图楼层平面前馈网络(MvFFN),用于从稀疏、无姿态的RGB全景图中重建准确的相机姿态和楼层平面布局。该新方法侧重于局部预测,特别是使用多视图有序墙体实例分割(MOIS)来预测墙体及其相邻部分。然后,系统聚合这些局部预测,形成全局墙体分区、房间布局和共享坐标,在墙体和连接点精度方面优于先前的方法。还引入了一个名为ZInD-CrossView的新数据集来支持此任务。 AI

影响 这项研究推进了用于从图像进行3D重建的计算机视觉技术,可能对机器人和增强现实等领域产生影响。

排序理由 该集群包含一篇详细介绍计算机视觉任务新方法的学术论文。

在 arXiv cs.AI 阅读 →

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新的MvFFN方法从全景图像重建楼层平面

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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yuguang Li, Yichuan Deng, Zixuan Liu, Ivaylo Boyadzhiev, Xiuyuan Wu, Xinyu Zou, Linda Shapiro, Alex Colburn ·

    MvFFN:用于无姿态宽基线全景布局重建的多视图前馈网络

    arXiv:2610.14315v2 Announce Type: cross Abstract: Reconstructing accurate camera poses and floor-plan layouts from sparse, unposed, wide-baseline RGB panoramas remains a challenging open problem. Direct multi-room floor-plan prediction is especially difficult in this setting: pos…