论文标题
变形隐式场：用学习的密度对应关系建模3D形状
Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondence
论文作者
论文摘要
我们提出了一种新型的变形隐式场（DIF）表示，用于建模类别的3D形状并在形状之间产生密集的对应关系。使用DIF，一个3D形状由整个类别共享的模板隐式字段以及3D变形字段和每个形状实例专用的校正字段表示。可以使用其变形字段轻松建立形状对应关系。我们的神经网络被称为dif-net，共同学习一个形状的潜在空间，而这些属于类别的3D对象的这些字段，而无需使用任何对应关系或零件标签。学习的差异还可以提供可靠的对应不确定性测量，以反映形状结构差异。实验表明，DIF-NET不仅产生高保真3D形状，而且还产生了不同形状的高质量密度对应关系。我们还演示了几种应用，例如纹理传输和形状编辑，我们的方法可以实现以前方法无法实现的引人注目的结果。
We propose a novel Deformed Implicit Field (DIF) representation for modeling 3D shapes of a category and generating dense correspondences among shapes. With DIF, a 3D shape is represented by a template implicit field shared across the category, together with a 3D deformation field and a correction field dedicated for each shape instance. Shape correspondences can be easily established using their deformation fields. Our neural network, dubbed DIF-Net, jointly learns a shape latent space and these fields for 3D objects belonging to a category without using any correspondence or part label. The learned DIF-Net can also provides reliable correspondence uncertainty measurement reflecting shape structure discrepancy. Experiments show that DIF-Net not only produces high-fidelity 3D shapes but also builds high-quality dense correspondences across different shapes. We also demonstrate several applications such as texture transfer and shape editing, where our method achieves compelling results that cannot be achieved by previous methods.
