用图像先验生成网格交叉场，实现语义对齐的四边形网格化。
Look Both Ways Before You Cross: Lifting Cross Fields From 2D Visual Priors
- 基于2D图像先验提取像素方向，反投影到网格表面。
- 多视角融合与自适应加权，解决遮挡与方向冲突问题。
- 支持交互设计与纹理对齐，适用于复杂有机/机械模型。
我们提出CrossLift，一种利用图像中视觉特征引导网格上计算交叉场的方法。通过强大的文本到图像先验，合成具有特征对齐四边形网格的2D图像，从中提取显式的每像素方向，并将其反投影至网格表面。在网格上进行两次平滑插值：先在单视图内，再跨多视图融合。引入基于置信度的自定义权重，以解决同一面上候选方向的冲突，并平滑地推广至遮挡面。该方法模块化，可兼容多种2D视觉先验。我们展示了其在纹理对齐四边形网格化及交互式交叉场设计中的应用，用户可用粗略草图作为信号输入。在多样化的有机与机械形状上验证了有效性，生成的四边形网格在语义对齐方面优于现有方法。
原文摘要 · Abstract (English)
We present CrossLift, a technique for computing cross fields on meshes guided by visual features in images. We leverage powerful text-to-image priors that are capable of synthesizing images of feature-aligned quad meshes in 2D. We extract this signal as explicit per-pixel directions in the 2D images, which we then back-project to the mesh surface. We aggregate these candidate surface directions by performing two smooth interpolations on the mesh surface (first within each view and second across multiple views). We propose custom confidence-based weights for the candidate directions in each interpolation that allow us to resolve conflicts between candidates on the same face and smoothly interpolate our field to occluded faces. Our method is modular and can be used with many different 2D visual priors. We show additional applications to texture-aligned quad meshing as well as interactive cross-field design using coarse, user-drawn lines as signal. We demonstrate the effectiveness of CrossLift on a diverse set of both organic and mechanical shapes and produce quad meshes that exhibit superior semantic alignment as compared to existing methods. Project page at: https://crosslift.github.io/
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