Active Surface Models have a long history of being useful to model complex 3D surfaces but only Active Contours have been used in conjunction with deep networks, and then only to produce the data term as well as meta-parameter maps controlling them. In this paper, we advocate a much tighter integration. We introduce layers that implement them that can be integrated seamlessly into Graph Convolutional Networks to enforce sophisticated smoothness priors at an acceptable computational cost. We will show that the resulting Deep Active Surface Models outperform equivalent architectures that use traditional regularization loss terms to impose smoothness priors for 3D surface reconstruction from 2D images and for 3D volume segmentation.
翻译:活跃表面模型长期以来一直有用于模拟复杂的 3D 表面,但只有活表面模型才与深层网络结合使用,然后仅用于生成数据术语和元参数图。在本文件中,我们主张更紧密地整合。我们引入了可以无缝地融入图层变迁网络的层层,以便以可接受的计算成本执行复杂的平滑前期。我们将显示,由此形成的深活表面模型优于类似的结构,这些结构使用传统的正规化损失术语,对二维图像和三维体积分解的三维表面重建进行平滑前置。