论文标题
在歧管对象的2D线框投影中的神经面部识别
Neural Face Identification in a 2D Wireframe Projection of a Manifold Object
论文作者
论文摘要
在计算机辅助设计（CAD）系统中，2D线图通常用于说明3D对象设计。要重建由单个2D线图描绘的3D模型，一个重要的关键是在线图中找到与3D对象的实际面相对应的线图中的边缘循环。在本文中，我们从新颖的数据驱动的角度解决了面部识别的经典问题。我们将其作为一个序列生成问题进行了说明：从任意边缘开始，我们采用流行变压器模型的变体来预测自然顺序与同一面相关的边缘。这使我们能够避免像大多数现有方法一样，使用各种手工制作的规则和启发式方法搜索所有可能的边缘循环的空间，处理诸如弯曲表面和嵌套边缘环等具有挑战性的案例，并利用其他提示（例如面部类型）。我们进一步讨论如何将预测不完美用于3D对象重建。
In computer-aided design (CAD) systems, 2D line drawings are commonly used to illustrate 3D object designs. To reconstruct the 3D models depicted by a single 2D line drawing, an important key is finding the edge loops in the line drawing which correspond to the actual faces of the 3D object. In this paper, we approach the classical problem of face identification from a novel data-driven point of view. We cast it as a sequence generation problem: starting from an arbitrary edge, we adopt a variant of the popular Transformer model to predict the edges associated with the same face in a natural order. This allows us to avoid searching the space of all possible edge loops with various hand-crafted rules and heuristics as most existing methods do, deal with challenging cases such as curved surfaces and nested edge loops, and leverage additional cues such as face types. We further discuss how possibly imperfect predictions can be used for 3D object reconstruction.
