In this paper, we propose a novel method, which we call Consistent ZoomOut, for efficiently refining correspondences among deformable 3D shape collections, while promoting the resulting map consistency. Our formulation is closely related to a recent unidirectional spectral refinement framework, but naturally integrates map consistency constraints into the refinement. Beyond that, we show further that our formulation can be adapted to recover the underlying isometry among near‐isometric shape collections with a theoretical guarantee, which is absent in the other spectral map synchronization frameworks. We demonstrate that our method improves the accuracy compared to the competing methods when synchronizing correspondences in both near‐isometric and heterogeneous shape collections, but also significantly outperforms the baselines in terms of map consistency.

中文翻译：

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一致的缩放输出：高效的光谱图同步

在本文中，我们提出了一种称为 Consistent ZoomOut 的新方法，用于有效地细化可变形 3D 形状集合之间的对应关系，同时提高生成的地图一致性。我们的公式与最近的单向光谱细化框架密切相关，但自然地将地图一致性约束集成到细化中。除此之外，我们进一步表明，我们的公式可以适用于在具有理论保证的近等距形状集合中恢复潜在的等距，这在其他光谱图同步框架中是不存在的。我们证明，在同步近等距和异构形状集合中的对应关系时，我们的方法与竞争方法相比提高了准确性，